{"schema_version":"xebench.release.v1","generated_at":"2026-10-05","generator":"scripts/export_benchmark_release.py","release":null,"minimum_trials":40,"minimum_bodies":3,"sources":[{"id":"registry","path":"src/assets/benchmark/registry.json","provenance":"demo","note":"Closed-loop rig: policy families and proposed bodies; example grid; no RC release yet."},{"id":"xe-table","path":"src/assets/xe/xe-table.json","provenance":"demo","note":"Every score, rate and count is seeded demonstration data. No real runs. Task reference videos are real footage."},{"id":"offline","path":"src/assets/benchmark/offline-leaderboard.json","provenance":"synthetic","note":"Example values. These rows come from synthetic pipeline checks, not measured policies. No external submission has been scored yet."},{"id":"transfer","path":"data/xe-transfer-robocoin.json","provenance":"measured","note":"RC research on whole-robot swaps (three real bimanual robots with their stock grippers) on one task. Not an XEbench submission, not closed-loop task success, and no dexterous-hand result exists yet."},{"id":"community","path":"Supabase dexbench_leaderboard (browser only)","provenance":"measured","note":"Self-reported community runs; mapped to records in the browser; not RC-verified."}],"legend":{"protocol":{"closed_loop":{"label":"Closed-loop · real rig","desc":"The policy controls the robot on the bench; each trial succeeds or fails. Success rate with a 95% Wilson interval; human-relative throughput when a matched teleoperation reference exists.","version":"0.1.0"},"open_loop_offline":{"label":"Open-loop · offline submissions","desc":"RC runs a submitted policy in an isolated container on a fixed held-out split and scores its predicted action chunks against recorded actions. Error is not task success.","version":"xebench-openloop-v1"},"transfer_loeo":{"label":"Transfer · leave-one-embodiment-out","desc":"Train on the other robots' data, fine-tune on the held-out robot's own demos, compare with training on those demos alone. Gain = (1 − err_transfer / err_scratch) × 100 on open-loop translation error. 95% interval: a t-interval combining between-seed variance with an episode-bootstrap variance.","version":"xe-0.1-loeo"}},"provenance":{"measured":"Recorded on real hardware and scored by the stated protocol.","demo":"Hand-constructed or seeded example values that show the format. Not a measurement.","synthetic":"Produced by a synthetic pipeline check. Not a measurement.","sim":"Produced in simulation. Not a real-robot measurement."},"status":{"final":"Reviewed and frozen.","preliminary":"Measured, may still change (runs finishing or flags open).","refused":"Computed, but RC's own guardrails block quoting it; shown with the reason.","pending":"Not measured yet."}},"policies":[{"id":"policy-a","name":"Policy A","detail":"Example · anonymised","kind":"learned","example":true},{"id":"policy-b","name":"Policy B","detail":"Example · anonymised","kind":"learned","example":true},{"id":"policy-c","name":"Policy C","detail":"Example · anonymised","kind":"learned","example":true},{"id":"policy-d","name":"Policy D","detail":"Example · anonymised","kind":"learned","example":true},{"id":"llm-a","name":"LLM controller A","detail":"Example · anonymised","kind":"llm","example":true},{"id":"llm-b","name":"LLM controller B","detail":"Example · anonymised","kind":"llm","example":true},{"id":"llm-c","name":"LLM controller C","detail":"Example · anonymised","kind":"llm","example":true}],"supported_policy_families":[{"id":"pi0","name":"π₀","detail":"Physical Intelligence","source":"https://github.com/Physical-Intelligence/openpi","kind":"learned","details":{"kind_label":"Vision-language-action","summary":"OpenPI implementations. Record the exact checkpoint, fine-tuning data and action adapter.","source":"https://github.com/Physical-Intelligence/openpi"}},{"id":"pi05","name":"π₀.₅","detail":"Physical Intelligence","source":"https://github.com/Physical-Intelligence/openpi","kind":"learned","details":{"kind_label":"Vision-language-action","summary":"OpenPI implementations. Record the exact checkpoint, fine-tuning data and action adapter.","source":"https://github.com/Physical-Intelligence/openpi"}},{"id":"act","name":"ACT","detail":"Imitation baseline","source":"https://github.com/tonyzhaozh/act","kind":"learned","details":{"kind_label":"Imitation learning","summary":"A task-trained imitation baseline. Document demonstrations, training budget and action space.","source":"https://github.com/tonyzhaozh/act"}},{"id":"groot-n16","name":"GR00T","detail":"N1.6 · NVIDIA","source":"https://github.com/NVIDIA/Isaac-GR00T","kind":"learned","details":{"kind_label":"Robot foundation model","summary":"N1.6 is the proposed reference version. Pin the model revision and embodiment configuration.","source":"https://github.com/NVIDIA/Isaac-GR00T"}},{"id":"gpt-6-astra","name":"GPT-6 Astra","detail":"OpenAI · LLM controller","kind":"llm","source":"https://developers.openai.com/api/docs/models/gpt-6-astra","integration":"XEbench includes an arrow-key controller with bounded finger actions. Other tasks require a task-specific controller and robot adapter. Pin the model revision and prompt.","verified":"2026-10-05: https://developers.openai.com/api/docs/models/gpt-6-astra returns 200 and the model is listed on the OpenAI models index.","details":{"kind_label":"LLM controller","summary":"XEbench includes an arrow-key controller with bounded finger actions. Other tasks require a task-specific controller and robot adapter. Pin the model revision and prompt.","source":"https://developers.openai.com/api/docs/models/gpt-6-astra"}},{"id":"gemini","name":"Gemini","detail":"Google · model pinned per run","kind":"llm","source":"https://ai.google.dev/gemini-api/docs/models","integration":"A task-specific controller and robot adapter are required. Record the exact Gemini model ID, prompt, observation format, and control frequency.","details":{"kind_label":"LLM controller","summary":"A task-specific controller and robot adapter are required. Record the exact Gemini model ID, prompt, observation format, and control frequency.","source":"https://ai.google.dev/gemini-api/docs/models"}},{"id":"claude","name":"Claude","detail":"Anthropic · model pinned per run","kind":"llm","source":"https://platform.claude.com/docs/en/models/overview","integration":"A task-specific controller and robot adapter are required. Record the exact Claude model ID, prompt, observation format, and control frequency.","details":{"kind_label":"LLM controller","summary":"A task-specific controller and robot adapter are required. Record the exact Claude model ID, prompt, observation format, and control frequency.","source":"https://platform.claude.com/docs/en/models/overview"}}],"bodies":[{"id":"body-1","arm":"Arm 1","hand":"Hand 1","brand":"Body 1","tasks":["pick-place-ab"],"example":true,"end_effector_type":"hand"},{"id":"body-2","arm":"Arm 2","hand":"Hand 2","brand":"Body 2","tasks":["pick-place-ab"],"example":true,"end_effector_type":"hand"},{"id":"body-3","arm":"Arm 3","hand":"Hand 3","brand":"Body 3","tasks":["pick-place-ab"],"example":true,"end_effector_type":"hand"},{"id":"body-4","arm":"Arm 4","hand":"Hand 4","brand":"Body 4","tasks":["pick-place-ab"],"example":true,"end_effector_type":"hand"},{"id":"body-5","arm":"Arm 5","hand":"Hand 5","brand":"Body 5","tasks":["pick-place-ab"],"example":true,"end_effector_type":"hand"},{"id":"body-6","arm":"Arm 6","hand":"Hand 6","brand":"Body 6","tasks":["pick-place-ab","keypress-ldr","piano-seq"],"example":true,"end_effector_type":"hand"},{"id":"body-7","arm":"Arm 7","hand":"Hand 7","brand":"Body 7","tasks":["pick-place-ab","keypress-ldr","piano-seq"],"example":true,"end_effector_type":"hand"},{"id":"body-8","arm":"Arm 8","hand":"Hand 8","brand":"Body 8","tasks":["pick-place-ab","keypress-ldr","piano-seq"],"example":true,"end_effector_type":"hand"},{"id":"body-9","arm":"Arm 9","hand":"Hand 9","brand":"Body 9","tasks":["pick-place-ab","keypress-ldr","piano-seq"],"example":true,"end_effector_type":"hand"},{"id":"body-10","arm":"Arm 10","hand":"Gripper 10","brand":"Body 10","tasks":["pick-place-ab"],"example":true,"end_effector_type":"gripper"},{"id":"body-11","arm":"Arm 11","hand":"Gripper 11","brand":"Body 11","tasks":["pick-place-ab"],"example":true,"end_effector_type":"gripper"}],"proposed_bodies":[{"id":"marvin-pro-m6-wuji","arm":"TianJi Marvin Pro (M6)","hand":"Wuji Hand","brand":"TianJi × Wuji","image":"/assets/products/wuji-hand.webp","tasks":["pick-place-ab"],"arm_image":"/assets/arms/tianji-marvin-pro.webp","details":{"arm":{"name":"TianJi Marvin Pro","brand":"TianJi","image":"/assets/arms/tianji-marvin-pro.webp","page":"https://dexterity.roboticscenter.ai/systems?arm=tianji-marvin-pro#configure","facts":[{"label":"Payload","value":"6 kg per arm (M6)"},{"label":"Reach","value":"696 mm (M6)"}]},"hand":{"name":"Wuji Dexterous Hand","brand":"Wuji","image":"/assets/products/wuji-hand.webp","page":"https://dexterity.roboticscenter.ai/hands#wuji-hand","facts":[{"label":"Degrees of freedom","value":"20 active"},{"label":"Actuation","value":"Self-locking rotary direct drive"},{"label":"Weight","value":"580 ±10 g, excluding cables"},{"label":"Fingertip force","value":"15 N"}],"spec_note":"Original Wuji Hand specifications. Other Wuji generations have different interfaces.","spec_source":"https://docs.wuji.tech/docs/en/wuji-hand/v1/overview/"},"tasks":[{"id":"pick-place-ab","title":"Pick and place"}]}},{"id":"fr3-wuji","arm":"Franka FR3","hand":"Wuji Hand","brand":"Franka × Wuji","image":"/assets/products/wuji-hand.webp","tasks":["pick-place-ab"],"arm_image":"/assets/arms/franka-fr3.webp","details":{"arm":{"name":"Franka FR3","image":"/assets/arms/franka-fr3.webp","facts":[]},"hand":{"name":"Wuji Dexterous Hand","brand":"Wuji","image":"/assets/products/wuji-hand.webp","page":"https://dexterity.roboticscenter.ai/hands#wuji-hand","facts":[{"label":"Degrees of freedom","value":"20 active"},{"label":"Actuation","value":"Self-locking rotary direct drive"},{"label":"Weight","value":"580 ±10 g, excluding cables"},{"label":"Fingertip force","value":"15 N"}],"spec_note":"Original Wuji Hand specifications. Other Wuji generations have different interfaces.","spec_source":"https://docs.wuji.tech/docs/en/wuji-hand/v1/overview/"},"tasks":[{"id":"pick-place-ab","title":"Pick and place"}]}},{"id":"marvin-pro-m6-sharpa","arm":"TianJi Marvin Pro (M6)","hand":"Sharpa Wave","brand":"TianJi × Sharpa","image":"/assets/products/sharpa-sharpa-wave.webp","tasks":["pick-place-ab"],"arm_image":"/assets/arms/tianji-marvin-pro.webp","details":{"arm":{"name":"TianJi Marvin Pro","brand":"TianJi","image":"/assets/arms/tianji-marvin-pro.webp","page":"https://dexterity.roboticscenter.ai/systems?arm=tianji-marvin-pro#configure","facts":[{"label":"Payload","value":"6 kg per arm (M6)"},{"label":"Reach","value":"696 mm (M6)"}]},"hand":{"name":"Sharpa Wave","brand":"Sharpa","image":"/assets/products/sharpa-sharpa-wave.webp","page":"https://dexterity.roboticscenter.ai/hands#sharpa-sharpa-wave","facts":[{"label":"Degrees of freedom","value":"22 active"},{"label":"Actuation","value":"22 proprietary actuation modules"},{"label":"Weight","value":"1.3 kg"},{"label":"Fingertip force","value":"12–20 N, by version"}],"spec_note":"Force and payload vary between Wave versions; confirm the exact revision before comparing those ratings.","spec_source":"https://www.sharpa.com/pages/wave"},"tasks":[{"id":"pick-place-ab","title":"Pick and place"}]}},{"id":"fr3-sharpa","arm":"Franka FR3","hand":"Sharpa Wave","brand":"Franka × Sharpa","image":"/assets/products/sharpa-sharpa-wave.webp","tasks":["pick-place-ab"],"arm_image":"/assets/arms/franka-fr3.webp","details":{"arm":{"name":"Franka FR3","image":"/assets/arms/franka-fr3.webp","facts":[]},"hand":{"name":"Sharpa Wave","brand":"Sharpa","image":"/assets/products/sharpa-sharpa-wave.webp","page":"https://dexterity.roboticscenter.ai/hands#sharpa-sharpa-wave","facts":[{"label":"Degrees of freedom","value":"22 active"},{"label":"Actuation","value":"22 proprietary actuation modules"},{"label":"Weight","value":"1.3 kg"},{"label":"Fingertip force","value":"12–20 N, by version"}],"spec_note":"Force and payload vary between Wave versions; confirm the exact revision before comparing those ratings.","spec_source":"https://www.sharpa.com/pages/wave"},"tasks":[{"id":"pick-place-ab","title":"Pick and place"}]}},{"id":"yam-wuji","arm":"I2RT YAM","hand":"Wuji Hand","brand":"I2RT × Wuji","image":"/assets/products/wuji-hand-2.webp","tasks":["pick-place-ab"],"arm_image":"/assets/arms/i2rt-yam-6dof.webp","details":{"arm":{"name":"i2rt YAM 6-DOF Robot Arm","brand":"I2RT","image":"/assets/arms/i2rt-yam-6dof.webp","page":"https://dexterity.roboticscenter.ai/systems?arm=i2rt-yam-6dof#configure","facts":[{"label":"Payload","value":"2 kg"},{"label":"Reach","value":"780 mm"}]},"hand":{"name":"Wuji Dexterous Hand","brand":"Wuji","image":"/assets/products/wuji-hand.webp","page":"https://dexterity.roboticscenter.ai/hands#wuji-hand","facts":[{"label":"Degrees of freedom","value":"20 active"},{"label":"Actuation","value":"Self-locking rotary direct drive"},{"label":"Weight","value":"580 ±10 g, excluding cables"},{"label":"Fingertip force","value":"15 N"}],"spec_note":"Original Wuji Hand specifications. 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confirm the exact revision before comparing those ratings.","spec_source":"https://www.sharpa.com/pages/wave"},"tasks":[{"id":"pick-place-ab","title":"Pick and place"},{"id":"keypress-ldr","title":"Arrow-key sequence"},{"id":"piano-seq","title":"Piano sequence"}]}},{"id":"piper-gripper","arm":"AgileX PiPER","hand":"Parallel gripper","brand":"AgileX","image":"/assets/arms/agilex-piper.webp","tasks":["pick-place-ab"],"arm_image":"/assets/arms/agilex-piper.webp","details":{"arm":{"name":"AgileX PiPER Robot Arm","brand":"AgileX","image":"/assets/arms/agilex-piper.webp","page":"https://dexterity.roboticscenter.ai/systems?arm=agilex-piper#configure","facts":[{"label":"Payload","value":"1.5 kg"},{"label":"Reach","value":"626 mm"}]},"hand":{"name":"Parallel gripper","image":"/assets/arms/agilex-piper.webp","facts":[]},"tasks":[{"id":"pick-place-ab","title":"Pick and place"}]}},{"id":"yam-gripper","arm":"I2RT YAM","hand":"Linear 4310 gripper","brand":"I2RT","image":"/assets/arms/i2rt-yam-6dof.webp","tasks":["pick-place-ab"],"arm_image":"/assets/arms/i2rt-yam-6dof.webp","details":{"arm":{"name":"i2rt YAM 6-DOF Robot Arm","brand":"I2RT","image":"/assets/arms/i2rt-yam-6dof.webp","page":"https://dexterity.roboticscenter.ai/systems?arm=i2rt-yam-6dof#configure","facts":[{"label":"Payload","value":"2 kg"},{"label":"Reach","value":"780 mm"}]},"hand":{"name":"Linear 4310 gripper","image":"/assets/arms/i2rt-yam-6dof.webp","facts":[]},"tasks":[{"id":"pick-place-ab","title":"Pick and place"}]}}],"tasks":[{"id":"pick-place-ab","title":"Pick and place","trials":30,"scope":"arm + hand","protocol":"closed_loop","details":{"summary":"Evaluate the complete arm + hand system. Move a ball, cube, or marker from zone A into the tray: 10 trials per object.","trials":30,"scope":"arm + hand","setup_guide":"https://github.com/RoboticsCenter/XEbench/blob/main/hardware/pick-place-ab.md","poster":"/assets/benchmark/pick-place-ab-rig.webp"}},{"id":"keypress-ldr","title":"Arrow-key sequence","trials":20,"scope":"hand + fixture","protocol":"closed_loop","details":{"summary":"Press LEFT, DOWN, then RIGHT with three assigned fingers.","trials":20,"scope":"hand + fixture","setup_guide":"https://github.com/RoboticsCenter/XEbench/blob/main/hardware/keypress-ldr.md","poster":"/assets/benchmark/keypress-ldr-poster.webp"}},{"id":"piano-seq","title":"Piano sequence","trials":20,"scope":"hand + fixture","protocol":"closed_loop","details":{"summary":"Play MIDI notes 60, 62, then 64 with even, medium force.","trials":20,"scope":"hand + fixture","setup_guide":"https://github.com/RoboticsCenter/XEbench/blob/main/hardware/piano-seq.md","poster":"/assets/benchmark/piano-seq-poster.webp"}},{"id":"xe-scenes","title":"Scene suite (RC Instrument)","trials":40,"scope":"arm + hand","protocol":"closed_loop"}],"transfer":{"model":"XE-0.1","dataset":"RoboCOIN","task":"stack_baskets","as_of":"2026-10-05","metric":"open-loop translation error (trans_err_m)","seeds":3,"eval_episodes":30,"licence":"Apache-2.0","licence_source":"RoboCOIN dataset cards for the three stack_baskets repos say 'license: apache-2.0' (read 2026-10-01); recorded in xe-model xe/data/converters/lerobot.py:81-101.","robots":[{"id":"cobot","name":"Agilex Cobot Magic","short":"Cobot Magic","shape":"circle"},{"id":"r1lite","name":"Galaxea R1 Lite","short":"R1 Lite","shape":"square"},{"id":"rmc","name":"Realman RMC-AIDA-L","short":"RMC-AIDA-L","shape":"triangle"}],"protocols":{"legacy":"Unequal learning rates for the two conditions (transfer vs scratch); single-sample decoding.","fair":"Each condition's (transfer vs scratch) learning rate chosen on validation; deterministic (zero_noise) decoding."},"headline":{"status":"cleared","reason":"All guard flags are clear: every chosen learning rate sits inside its grid and no run diverged."},"pending_note":"All transfer values are preliminary and may still change.","not_retested_label":"XE-0.1; not re-tested with normalised targets","flags_legend":{},"scope_note":"RC research on whole-robot swaps (three real bimanual robots with their stock grippers) on one task. Not an XEbench submission, not closed-loop task success, and no dexterous-hand result exists yet.","claims":{"can":["On one task (stack_baskets) across three real bimanual robots, pretraining on the other two robots lowered the macro held-out translation error at 20 demos under both protocols. Per robot, R1 Lite and RMC-AIDA-L show a gain with an interval above zero under the fair protocol. Cobot Magic's interval with three seeds (+3.7% [-4.0, +11.4]) included zero; with eight seeds (pre-registered follow-up) it is +4.2% [+1.0, +7.4], above zero. The fair macro uses the three-seed Cobot Magic cell. A pre-registered replication on a second task (fold_clothes; Cobot Magic, Galbot G1, R1 Lite) gives a fair macro gain of +11.6% [+9.7, +13.4] at 20 demos; the scratch arm is still on a loss plateau at the registered 3000 steps. With a pre-registered 10000-step fine-tune, where it clears the plateau, the gain is +5.8% [+3.3, +8.3]: smaller, still above zero, so part of the 3000-step gain was faster optimisation.","Under the fair protocol XE-0.1's macro gain at 20 demos is +8.7% [+5.8, +11.5], an interval above zero. In a pre-registered follow-up, XE-0.2 (end-effector action targets normalised) gives +2.2% [-0.9, +5.2] on the same cells: not a demonstrated gain. The change from XE-0.1, -6.5 pp [-10.3, -2.6], is confounded by code drift between the two versions. Do not carry the XE-0.1 number over to XE-0.2.","Under the fair protocol (pre-registered follow-up) the macro gain is +19.7% [+10.8, +28.6] at 5 demos, +8.7% [+5.8, +11.5] at 20 and +6.4% [+4.5, +8.2] at 50 (47 for R1 Lite); all preliminary. To match transfer at 20 demos, scratch needs more than 47 demos on R1 Lite (95% CI lower bound 40) and 32 [23, 46] on RMC-AIDA-L; on Cobot Magic the answer is not resolved (26 [14, 50]). A fixed-step control could not separate more demos from more training steps.","At the macro level, fair learning rates alone move the gain from 14.3% to 14.7%; switching to deterministic decoding moves it to 8.7%. Most of the shrink comes from how actions are decoded."],"cannot":["Anything about dexterous hands. No dexterous-hand result exists yet.","Task success. The metric is open-loop translation error, not closed-loop success on a real robot.","Generality beyond two tasks (stack_baskets, fold_clothes) and four robots. The second task replicates the macro gain; its per-robot pattern differs.","The end-effector swap study. It is not part of this publication."]}},"records":[
{"id":"demo:pick-place-ab:body-1:policy-a:sr","protocol":"closed_loop","task":"pick-place-ab","metric":"success_rate","unit":"fraction","higher_is_better":true,"value":0.6667,"ci_low":0.5406,"ci_high":0.7727,"ci_method":"wilson95","k":40,"n_episodes":60,"n_excluded":0,"policy":"Policy A","policy_id":"policy-a","policy_kind":"learned","owner":"external","body_id":"body-1","body_label":"Arm 1 + Hand 1","arm":"Arm 1","end_effector":"Hand 1","end_effector_type":"hand","provenance":"demo","verification":"none","status":"final","licence_track":"n/a","date":"2026-09-28","source":"Example grid constructed for the page (not collected from robots)","extra":{"elapsed_s":1210,"protocol_trials":60}},
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{"id":"tr:fair:r1lite:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":15.7,"ci_low":10.0,"ci_high":21.4,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"r1lite","body_label":"Galaxea R1 Lite","arm":"Galaxea R1 Lite","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 transfer study on RoboCOIN (stack_baskets)","note":"Clean: no flags.","extra":{"variant":"fair","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)"}},
{"id":"tr:fair:rmc:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":6.6,"ci_low":1.3,"ci_high":11.9,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"rmc","body_label":"Realman RMC-AIDA-L","arm":"Realman RMC-AIDA-L","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 transfer study on RoboCOIN (stack_baskets)","note":"Clean: no flags.","extra":{"variant":"fair","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)"}},
{"id":"tr:fair:macro:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":8.7,"ci_low":5.8,"ci_high":11.5,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 transfer study on RoboCOIN (stack_baskets)","note":"Headline gate clear.","extra":{"variant":"fair","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)"}},
{"id":"tr:fair1:macro:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":14.7,"ci_low":12.1,"ci_high":17.3,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 transfer study on RoboCOIN (stack_baskets)","note":"Same fair LRs, single-sample decoding. The gap to 8.7 is the decoding effect.","extra":{"variant":"fair_single_sample","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)"}},
{"id":"tr:fair:cobot:20:s8","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":4.2,"ci_low":1.0,"ci_high":7.4,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":8,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"cobot","body_label":"Agilex Cobot Magic","arm":"Agilex Cobot Magic","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered follow-up, 8 seeds (the 3-seed cell in the fair protocol is +3.7 [-4.0, +11.4]). The fair macro still uses the 3-seed Cobot Magic cell.","extra":{"variant":"fair_cobot_8seed","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"cobot_8seed","pre_registered":true,"follow_up":"8 seeds, pre-registered"}},
{"id":"tr:fair:macro:20:fold","protocol":"transfer_loeo","task":"fold_clothes","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":11.6,"ci_low":9.7,"ci_high":13.4,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots, fold_clothes)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (fold_clothes)","note":"Pre-registered replication on a second task (Cobot Magic, Galbot G1, R1 Lite), zero-noise decoding, 3000 fine-tune steps. The scratch arm is still on a loss plateau at the registered 3000 steps. With a pre-registered 10000-step fine-tune, where it clears the plateau, the gain is +5.8% [+3.3, +8.3]: smaller, still above zero.","extra":{"variant":"fair_fold_clothes","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"fold_clothes","pre_registered":true,"caveat":"the scratch arm is still on a loss plateau at the registered 3000 steps. With a pre-registered 10000-step fine-tune, where it clears the plateau, the gain is +5.8% [+3.3, +8.3]: smaller, still above zero","robots":["Cobot Magic","Galbot G1","R1 Lite"],"finetune_steps":3000}},
{"id":"tr:fair:macro:20:fold:10k","protocol":"transfer_loeo","task":"fold_clothes","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":5.8,"ci_low":3.3,"ci_high":8.3,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots, fold_clothes, 10000 steps)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (fold_clothes)","note":"Pre-registered plateau test, verdict PARTIAL: the same fold_clothes comparison with a 10000-step fine-tune for both arms instead of 3000. Every scratch run clears the loss plateau it is on at 3000 steps. The gain stays above zero but is smaller than +11.6% [+9.7, +13.4] at 3000 steps (paired change −5.7 pp [−9.1, −2.4]), so part of the 3000-step gain was faster optimisation. Learning rates were not re-tuned for the longer budget.","extra":{"variant":"fair_fold_plateau","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"fold_plateau","pre_registered":true,"caveat":"at 10000 fine-tune steps every scratch run has cleared the loss plateau it is on at 3000; learning rates were not re-tuned for the longer budget","robots":["Cobot Magic","Galbot G1","R1 Lite"],"finetune_steps":10000,"verdict":"PARTIAL (rule 3)","replaces_caveat_of":"tr:fair:macro:20:fold"}},
{"id":"tr:xe02:macro:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":2.2,"ci_low":-0.9,"ci_high":5.2,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.2","policy_id":"xe-0.2","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.2 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered follow-up: XE-0.2 is XE-0.1 with the end-effector twist targets normalised per robot; same cells, seeds, splits and fair protocol. Normalisation lowered the error of both arms, scratch more than transfer, and the gain is no longer demonstrated at 20 demos.","extra":{"variant":"fair_xe02","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"xe02_norm","pre_registered":true,"model_change":"end-effector twist targets normalised per robot (fitted on each run's own training split)","verdict":"NORMALISATION SHRINKS THE TRANSFER GAIN (rule 1)","follow_up":"XE-0.2, normalised end-effector targets","compared_with":"tr:fair:macro:20","delta_vs_xe01_pp":{"v":-6.5,"lo":-10.3,"hi":-2.6},"error_reduction_pct":{"transfer":{"v":15.5,"lo":13.1,"hi":17.8},"scratch":{"v":21.0,"lo":19.0,"hi":23.1}},"confounded_by_code_drift":true,"caveat":"the comparison with XE-0.1 spans two code versions and is confounded by code drift: one of three registered drift checks fell 0.24 mm outside XE-0.1's seed range"}},
{"id":"tr:datamix:q:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"pretraining_contrast_pct","unit":"%","higher_is_better":true,"value":0.8,"ci_low":-1.5,"ci_high":3.0,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, final after its one registered learning-rate extension round: about 4× more cross-embodiment pretraining data (1673 vs 420 episodes from the same 7 robots and 42 tasks) makes no material difference to the transfer gain at this scale (95% CI within ±4%), with pretraining compute held fixed. Same-task and mixed pretraining are not shown to beat other-task pretraining: both point estimates are negative and both intervals include 0.","extra":{"variant":"fair_datamix","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"contrast":"Q","arm_a":"q1680","arm_b":"q420","contrast_label":"Quantity: 1673 vs 420 pretraining episodes (same 7 robots, 42 tasks)","primary":true,"verdict":"NO MATERIAL DIFFERENCE (rule 3: CI inside ±4%)","margin_pct":4,"multiplicity":"none: the three primary contrasts are each tested at 95%, as registered","gain_definition":"gain of A over B = 1 - error(A) / error(B), paired by seed and test episode, macro over the two targets, zero-noise decoding, test split","robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:datamix:t1:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"pretraining_contrast_pct","unit":"%","higher_is_better":true,"value":-3.0,"ci_low":-7.4,"ci_high":1.3,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, final after its one registered learning-rate extension round: about 4× more cross-embodiment pretraining data (1673 vs 420 episodes from the same 7 robots and 42 tasks) makes no material difference to the transfer gain at this scale (95% CI within ±4%), with pretraining compute held fixed. Same-task and mixed pretraining are not shown to beat other-task pretraining: both point estimates are negative and both intervals include 0.","extra":{"variant":"fair_datamix","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"contrast":"T1","arm_a":"same","arm_b":"cross","contrast_label":"Same task from another robot vs other tasks (100 episodes each)","primary":true,"verdict":"INCONCLUSIVE","margin_pct":4,"multiplicity":"none: the three primary contrasts are each tested at 95%, as registered","gain_definition":"gain of A over B = 1 - error(A) / error(B), paired by seed and test episode, macro over the two targets, zero-noise decoding, test split","robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:datamix:t2:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"pretraining_contrast_pct","unit":"%","higher_is_better":true,"value":-2.4,"ci_low":-5.7,"ci_high":0.8,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, final after its one registered learning-rate extension round: about 4× more cross-embodiment pretraining data (1673 vs 420 episodes from the same 7 robots and 42 tasks) makes no material difference to the transfer gain at this scale (95% CI within ±4%), with pretraining compute held fixed. Same-task and mixed pretraining are not shown to beat other-task pretraining: both point estimates are negative and both intervals include 0.","extra":{"variant":"fair_datamix","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"contrast":"T2","arm_a":"mix","arm_b":"cross","contrast_label":"50/50 mix vs other tasks (100 episodes each)","primary":true,"verdict":"INCONCLUSIVE","margin_pct":4,"multiplicity":"none: the three primary contrasts are each tested at 95%, as registered","gain_definition":"gain of A over B = 1 - error(A) / error(B), paired by seed and test episode, macro over the two targets, zero-noise decoding, test split","robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:datamix:t3:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"pretraining_contrast_pct","unit":"%","higher_is_better":true,"value":0.6,"ci_low":-3.5,"ci_high":4.7,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, final after its one registered learning-rate extension round: about 4× more cross-embodiment pretraining data (1673 vs 420 episodes from the same 7 robots and 42 tasks) makes no material difference to the transfer gain at this scale (95% CI within ±4%), with pretraining compute held fixed. Same-task and mixed pretraining are not shown to beat other-task pretraining: both point estimates are negative and both intervals include 0.","extra":{"variant":"fair_datamix","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"contrast":"T3","arm_a":"mix","arm_b":"same","contrast_label":"50/50 mix vs same task (secondary)","primary":false,"verdict":"INCONCLUSIVE","margin_pct":4,"multiplicity":"none: the three primary contrasts are each tested at 95%, as registered","gain_definition":"gain of A over B = 1 - error(A) / error(B), paired by seed and test episode, macro over the two targets, zero-noise decoding, test split","robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:datamix:q420:scratch:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":5.4,"ci_low":2.1,"ci_high":8.7,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, descriptive (no verdict): pretraining on 420 episodes, 7 robots, 42 tasks, then fine-tuning, vs scratch on the same demos; macro over Cobot Magic and RMC-AIDA-L.","extra":{"variant":"fair_datamix_scratch","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"condition":"q420","descriptive":true,"robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:datamix:q1680:scratch:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":6.1,"ci_low":3.8,"ci_high":8.4,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, descriptive (no verdict): pretraining on 1673 episodes, 7 robots, 42 tasks, then fine-tuning, vs scratch on the same demos; macro over Cobot Magic and RMC-AIDA-L.","extra":{"variant":"fair_datamix_scratch","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"condition":"q1680","descriptive":true,"robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:datamix:same:scratch:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":3.9,"ci_low":1.8,"ci_high":5.9,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, descriptive (no verdict): pretraining on 100 episodes of stack_baskets from one other robot, then fine-tuning, vs scratch on the same demos; macro over Cobot Magic and RMC-AIDA-L.","extra":{"variant":"fair_datamix_scratch","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"condition":"same","descriptive":true,"robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:datamix:cross:scratch:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":6.7,"ci_low":3.0,"ci_high":10.5,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, descriptive (no verdict): pretraining on 100 episodes of 10 other tasks from the same robot, then fine-tuning, vs scratch on the same demos; macro over Cobot Magic and RMC-AIDA-L.","extra":{"variant":"fair_datamix_scratch","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"condition":"cross","descriptive":true,"robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:datamix:mix:scratch:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":4.4,"ci_low":0.7,"ci_high":8.1,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro-cobot-rmc","body_label":"Macro (Cobot Magic, RMC-AIDA-L)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-mix study, descriptive (no verdict): pretraining on 50/50 mix of same and cross, 100 episodes, then fine-tuning, vs scratch on the same demos; macro over Cobot Magic and RMC-AIDA-L.","extra":{"variant":"fair_datamix_scratch","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_mix","pre_registered":true,"condition":"mix","descriptive":true,"robots":["Cobot Magic","RMC-AIDA-L"]}},
{"id":"tr:octo:vs_transfer:cobot:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"baseline_contrast_pct","unit":"%","higher_is_better":true,"value":4.1,"ci_low":-1.5,"ci_high":9.6,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"Octo-Base 1.5","policy_id":"octo-base-1.5","policy_kind":"learned","owner":"rc_baseline","body_id":"cobot","body_label":"Agilex Cobot Magic","arm":"Agilex Cobot Magic","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · Octo-Base 1.5 (Baseline (RC-run)) vs XE-0.1, pre-registered, on RoboCOIN (stack_baskets)","note":"Pre-registered comparison, run by RC (Baseline (RC-run), not ranked): Octo-Base 1.5 fine-tuned with its authors' recipe at XE-0.1's demo and step budget, each side at its own validation-chosen learning rate, on the same cells, seeds and test episodes as XE-0.1. Open-loop translation error, not task success.","extra":{"variant":"octo_fair","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"octo_fair","pre_registered":true,"study":"octo-fair-v1","contrast":"vs_transfer","xe_arm":"transfer","primary":false,"verdict":"INCONCLUSIVE","margin_pct":4,"multiplicity":"none across the per-robot and secondary contrasts, as registered","gain_definition":"G = 1 - error(Octo-Base) / error(XE-0.1 arm), paired by seed and test episode, zero-noise decoding, test split; positive means Octo-Base has the lower error","err_octo_m":0.0562,"err_xe_m":0.0586,"lr_octo":0.0001,"lr_xe":0.001,"label":"Baseline (RC-run)"}},
{"id":"tr:octo:vs_transfer:r1lite:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"baseline_contrast_pct","unit":"%","higher_is_better":true,"value":6.6,"ci_low":-1.2,"ci_high":14.5,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"Octo-Base 1.5","policy_id":"octo-base-1.5","policy_kind":"learned","owner":"rc_baseline","body_id":"r1lite","body_label":"Galaxea R1 Lite","arm":"Galaxea R1 Lite","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · Octo-Base 1.5 (Baseline (RC-run)) vs XE-0.1, pre-registered, on RoboCOIN (stack_baskets)","note":"Pre-registered comparison, run by RC (Baseline (RC-run), not ranked): Octo-Base 1.5 fine-tuned with its authors' recipe at XE-0.1's demo and step budget, each side at its own validation-chosen learning rate, on the same cells, seeds and test episodes as XE-0.1. Open-loop translation error, not task success.","extra":{"variant":"octo_fair","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"octo_fair","pre_registered":true,"study":"octo-fair-v1","contrast":"vs_transfer","xe_arm":"transfer","primary":false,"verdict":"INCONCLUSIVE","margin_pct":4,"multiplicity":"none across the per-robot and secondary contrasts, as registered","gain_definition":"G = 1 - error(Octo-Base) / error(XE-0.1 arm), paired by seed and test episode, zero-noise decoding, test split; positive means Octo-Base has the lower error","err_octo_m":0.0387,"err_xe_m":0.0415,"lr_octo":0.0001,"lr_xe":0.0003,"label":"Baseline (RC-run)"}},
{"id":"tr:octo:vs_transfer:rmc:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"baseline_contrast_pct","unit":"%","higher_is_better":true,"value":10.2,"ci_low":4.2,"ci_high":16.3,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"Octo-Base 1.5","policy_id":"octo-base-1.5","policy_kind":"learned","owner":"rc_baseline","body_id":"rmc","body_label":"Realman RMC-AIDA-L","arm":"Realman RMC-AIDA-L","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · Octo-Base 1.5 (Baseline (RC-run)) vs XE-0.1, pre-registered, on RoboCOIN (stack_baskets)","note":"Pre-registered comparison, run by RC (Baseline (RC-run), not ranked): Octo-Base 1.5 fine-tuned with its authors' recipe at XE-0.1's demo and step budget, each side at its own validation-chosen learning rate, on the same cells, seeds and test episodes as XE-0.1. Open-loop translation error, not task success.","extra":{"variant":"octo_fair","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"octo_fair","pre_registered":true,"study":"octo-fair-v1","contrast":"vs_transfer","xe_arm":"transfer","primary":false,"verdict":"OCTO BETTER","margin_pct":4,"multiplicity":"none across the per-robot and secondary contrasts, as registered","gain_definition":"G = 1 - error(Octo-Base) / error(XE-0.1 arm), paired by seed and test episode, zero-noise decoding, test split; positive means Octo-Base has the lower error","err_octo_m":0.0361,"err_xe_m":0.0402,"lr_octo":0.0001,"lr_xe":0.0003,"label":"Baseline (RC-run)"}},
{"id":"tr:octo:vs_transfer:macro:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"baseline_contrast_pct","unit":"%","higher_is_better":true,"value":7.0,"ci_low":3.5,"ci_high":10.4,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"Octo-Base 1.5","policy_id":"octo-base-1.5","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · Octo-Base 1.5 (Baseline (RC-run)) vs XE-0.1, pre-registered, on RoboCOIN (stack_baskets)","note":"Pre-registered comparison, run by RC (Baseline (RC-run), not ranked): Octo-Base 1.5 fine-tuned with its authors' recipe at XE-0.1's demo and step budget, each side at its own validation-chosen learning rate, on the same cells, seeds and test episodes as XE-0.1. Open-loop translation error, not task success.","extra":{"variant":"octo_fair","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"octo_fair","pre_registered":true,"study":"octo-fair-v1","contrast":"vs_transfer","xe_arm":"transfer","primary":true,"verdict":"OCTO BETTER","margin_pct":4,"multiplicity":"none across the per-robot and secondary contrasts, as registered","gain_definition":"G = 1 - error(Octo-Base) / error(XE-0.1 arm), paired by seed and test episode, zero-noise decoding, test split; positive means Octo-Base has the lower error","label":"Baseline (RC-run)"}},
{"id":"tr:octo:vs_scratch:cobot:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"baseline_contrast_pct","unit":"%","higher_is_better":true,"value":7.6,"ci_low":-0.6,"ci_high":15.7,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"Octo-Base 1.5","policy_id":"octo-base-1.5","policy_kind":"learned","owner":"rc_baseline","body_id":"cobot","body_label":"Agilex Cobot Magic","arm":"Agilex Cobot Magic","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · Octo-Base 1.5 (Baseline (RC-run)) vs XE-0.1, pre-registered, on RoboCOIN (stack_baskets)","note":"Pre-registered comparison, run by RC (Baseline (RC-run), not ranked): Octo-Base 1.5 fine-tuned with its authors' recipe at XE-0.1's demo and step budget, each side at its own validation-chosen learning rate, on the same cells, seeds and test episodes as XE-0.1. Open-loop translation error, not task success.","extra":{"variant":"octo_fair","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"octo_fair","pre_registered":true,"study":"octo-fair-v1","contrast":"vs_scratch","xe_arm":"scratch","primary":false,"verdict":"INCONCLUSIVE","margin_pct":4,"multiplicity":"none across the per-robot and secondary contrasts, as registered","gain_definition":"G = 1 - error(Octo-Base) / error(XE-0.1 arm), paired by seed and test episode, zero-noise decoding, test split; positive means Octo-Base has the lower error","err_octo_m":0.0562,"err_xe_m":0.0608,"lr_octo":0.0001,"lr_xe":0.001,"label":"Baseline (RC-run)"}},
{"id":"tr:octo:vs_scratch:r1lite:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"baseline_contrast_pct","unit":"%","higher_is_better":true,"value":21.3,"ci_low":13.7,"ci_high":28.8,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"Octo-Base 1.5","policy_id":"octo-base-1.5","policy_kind":"learned","owner":"rc_baseline","body_id":"r1lite","body_label":"Galaxea R1 Lite","arm":"Galaxea R1 Lite","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · Octo-Base 1.5 (Baseline (RC-run)) vs XE-0.1, pre-registered, on RoboCOIN (stack_baskets)","note":"Pre-registered comparison, run by RC (Baseline (RC-run), not ranked): Octo-Base 1.5 fine-tuned with its authors' recipe at XE-0.1's demo and step budget, each side at its own validation-chosen learning rate, on the same cells, seeds and test episodes as XE-0.1. Open-loop translation error, not task success.","extra":{"variant":"octo_fair","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"octo_fair","pre_registered":true,"study":"octo-fair-v1","contrast":"vs_scratch","xe_arm":"scratch","primary":false,"verdict":"OCTO BETTER","margin_pct":4,"multiplicity":"none across the per-robot and secondary contrasts, as registered","gain_definition":"G = 1 - error(Octo-Base) / error(XE-0.1 arm), paired by seed and test episode, zero-noise decoding, test split; positive means Octo-Base has the lower error","err_octo_m":0.0387,"err_xe_m":0.0492,"lr_octo":0.0001,"lr_xe":0.0003,"label":"Baseline (RC-run)"}},
{"id":"tr:octo:vs_scratch:rmc:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"baseline_contrast_pct","unit":"%","higher_is_better":true,"value":16.2,"ci_low":8.0,"ci_high":24.3,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"Octo-Base 1.5","policy_id":"octo-base-1.5","policy_kind":"learned","owner":"rc_baseline","body_id":"rmc","body_label":"Realman RMC-AIDA-L","arm":"Realman RMC-AIDA-L","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · Octo-Base 1.5 (Baseline (RC-run)) vs XE-0.1, pre-registered, on RoboCOIN (stack_baskets)","note":"Pre-registered comparison, run by RC (Baseline (RC-run), not ranked): Octo-Base 1.5 fine-tuned with its authors' recipe at XE-0.1's demo and step budget, each side at its own validation-chosen learning rate, on the same cells, seeds and test episodes as XE-0.1. Open-loop translation error, not task success.","extra":{"variant":"octo_fair","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"octo_fair","pre_registered":true,"study":"octo-fair-v1","contrast":"vs_scratch","xe_arm":"scratch","primary":false,"verdict":"OCTO BETTER","margin_pct":4,"multiplicity":"none across the per-robot and secondary contrasts, as registered","gain_definition":"G = 1 - error(Octo-Base) / error(XE-0.1 arm), paired by seed and test episode, zero-noise decoding, test split; positive means Octo-Base has the lower error","err_octo_m":0.0361,"err_xe_m":0.043,"lr_octo":0.0001,"lr_xe":0.001,"label":"Baseline (RC-run)"}},
{"id":"tr:octo:vs_scratch:macro:20","protocol":"transfer_loeo","task":"stack_baskets","metric":"baseline_contrast_pct","unit":"%","higher_is_better":true,"value":15.0,"ci_low":10.9,"ci_high":19.1,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"Octo-Base 1.5","policy_id":"octo-base-1.5","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-10","source":"RC research · Octo-Base 1.5 (Baseline (RC-run)) vs XE-0.1, pre-registered, on RoboCOIN (stack_baskets)","note":"Pre-registered comparison, run by RC (Baseline (RC-run), not ranked): Octo-Base 1.5 fine-tuned with its authors' recipe at XE-0.1's demo and step budget, each side at its own validation-chosen learning rate, on the same cells, seeds and test episodes as XE-0.1. Open-loop translation error, not task success.","extra":{"variant":"octo_fair","scope":"macro","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"octo_fair","pre_registered":true,"study":"octo-fair-v1","contrast":"vs_scratch","xe_arm":"scratch","primary":false,"verdict":"OCTO BETTER","margin_pct":4,"multiplicity":"none across the per-robot and secondary contrasts, as registered","gain_definition":"G = 1 - error(Octo-Base) / error(XE-0.1 arm), paired by seed and test episode, zero-noise decoding, test split; positive means Octo-Base has the lower error","label":"Baseline (RC-run)"}},
{"id":"tr:fair:macro:5","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":19.7,"ci_low":10.8,"ci_high":28.6,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-efficiency study. At 5 demos the schedule is 2k steps vs 3k at 20, so demos and steps differ.","extra":{"variant":"fair_dataeff","scope":"macro","target_demos":5,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_efficiency","pre_registered":true}},
{"id":"tr:fair:macro:50","protocol":"transfer_loeo","task":"stack_baskets","metric":"transfer_gain_pct","unit":"%","higher_is_better":true,"value":6.4,"ci_low":4.5,"ci_high":8.2,"ci_method":"t95 (between-seed variance + episode bootstrap variance)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"macro","body_label":"Macro (3 held-out robots)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-efficiency study. 50 demos for Cobot Magic and RMC-AIDA-L, 47 for R1 Lite; 5k steps.","extra":{"variant":"fair_dataeff","scope":"macro","target_demos":50,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_efficiency","pre_registered":true}},
{"id":"tr:fair:cobot:demos_to_match","protocol":"transfer_loeo","task":"stack_baskets","metric":"demos_to_match","unit":"demos","higher_is_better":false,"value":26.1,"ci_low":13.7,"ci_high":49.7,"ci_method":"bootstrap over seeds × episodes (scaled schedule)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"cobot","body_label":"Agilex Cobot Magic","arm":"Agilex Cobot Magic","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-efficiency primary: scratch demos needed to match transfer at 20 demos (zero-noise, test). ","extra":{"variant":"fair_dataeff","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_efficiency","pre_registered":true,"at_transfer_demos":20,"verdict":"inconclusive","reading":"not resolved","lower_bound_only":false}},
{"id":"tr:fair:r1lite:demos_to_match","protocol":"transfer_loeo","task":"stack_baskets","metric":"demos_to_match","unit":"demos","higher_is_better":false,"ci_low":40.2,"ci_method":"bootstrap over seeds × episodes (scaled schedule)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"r1lite","body_label":"Galaxea R1 Lite","arm":"Galaxea R1 Lite","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-efficiency primary: scratch demos needed to match transfer at 20 demos (zero-noise, test). Censored: more than 47 demos (the top budget); only the lower bound of the interval is defined.","extra":{"variant":"fair_dataeff","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_efficiency","pre_registered":true,"at_transfer_demos":20,"verdict":"supported","reading":"more than 47","lower_bound_only":true,"more_than":47}},
{"id":"tr:fair:rmc:demos_to_match","protocol":"transfer_loeo","task":"stack_baskets","metric":"demos_to_match","unit":"demos","higher_is_better":false,"value":32.3,"ci_low":22.6,"ci_high":46.1,"ci_method":"bootstrap over seeds × episodes (scaled schedule)","n_episodes":30,"seeds":3,"policy":"XE-0.1","policy_id":"xe-0.1","policy_kind":"learned","owner":"rc_baseline","body_id":"rmc","body_label":"Realman RMC-AIDA-L","arm":"Realman RMC-AIDA-L","end_effector":"stock gripper (whole-robot swap)","end_effector_type":"gripper","provenance":"measured","verification":"rc_internal","status":"preliminary","licence_track":"Apache-2.0","date":"2026-10-05","source":"RC research · XE-0.1 pre-registered follow-up on RoboCOIN (stack_baskets)","note":"Pre-registered data-efficiency primary: scratch demos needed to match transfer at 20 demos (zero-noise, test). ","extra":{"variant":"fair_dataeff","scope":"per_robot","target_demos":20,"dataset":"RoboCOIN","base_metric":"open-loop translation error (trans_err_m)","followup":"data_efficiency","pre_registered":true,"at_transfer_demos":20,"verdict":"supported","reading":"supported","lower_bound_only":false}}
]}