common/source/starBenchserver/benchmarks/simBenchmarks/RoboDojo/EVALUATION_PROTOCOL.md

RoboDojo Direct v2 evaluation protocol

Protocol identity

The original identity below remains the historical reference deployment. An explicitly selected deployment_track: "official" uses the separately versioned official-source deployment: robodojo-official-hf91f76c28-rgb-proprio28-eef16-feedback-v1. Its public observation/action and native scoring are unchanged for each task; its asset and dependency provenance is different and must not be reported as reference parity.

• Benchmark: robodojo
• Public protocol: robodojo-direct-v2-rgb-proprio28-eef16-robot-feedback-v1
• Task: classify_objects
• Embodiment: two fixed-base ARX X5 arms
• Native task instruction: sort objects by category into three baskets
• Public instruction also requires both arms to return to their starting end-effector poses
• Campaign seed: 20260907
• Native evaluation/simulator seed: 0
• Panels: fast is layouts 0–4; full is layouts 0–49, in order
• Attempts: exactly one per selected layout; no automatic retry or replacement
• Native horizon: 1100 acknowledged controls per layout

The five-layout fast panel is the reference-comparison/development mode. The 50-layout full panel matches the upstream configured episode count for this task. Neither is the complete multi-task RoboDojo benchmark. A run must select one named panel before outcomes; arbitrary layout subsets are not a score.

The machine-readable definition is bench_server/protocol/task_catalog.json, validated by bench_server/protocol/task_catalog.py. It is benchmark protocol data, including the binding to the pinned layout inventory. It contains no native assets or operator deployment paths. server.py loads the service adapter, which applies this definition through the private runtime under environment/. Directory reorganization does not create a new protocol or alter the meaning of frozen historical results.

Public observation

At reset and after each acknowledged control the model receives:

• task language;
• original 640×480 uint8 RGB for cam_head, cam_left_wrist and cam_right_wrist;
• 28 robot values: six measured joints and one last commanded gripper opening per arm, followed by each measured link6 world pose as x,y,z,qw,qx,qy,qz.

After a control, the observation additionally contains the closed robodojo.robot-control-feedback/v1 acknowledgement: executed joint command, post-control public robot state, and robot-only DLS/FK diagnostics. Reset has no acknowledgement.

The model never receives object poses/identities, layout or reset metadata, camera calibration, contacts, depth, reward, native success, process score, scorer state, simulator handles, worker logs or private errors.

Public action

One action is 16 finite values:

[left_x, left_y, left_z, left_qw, left_qx, left_qy, left_qz, left_opening,
 right_x, right_y, right_z, right_qw, right_qx, right_qy, right_qz, right_opening]

Poses are absolute measured link6 targets in the declared world frame. Quaternions are absolute unit wxyz values with tolerance 1e-4; openings are continuous [0,1]. The benchmark performs a fixed robot-only numerical DLS conversion, applies native soft-limit/measured-joint bounds and sends exactly one native joint control. It does not plan around objects, choose targets, run a support policy or roll out hypothetical actions.

Model-side step size, rotation size, chunk length and ordinary give-up rules are policy configuration, not benchmark validity. Benchmark validity is limited to the declared vector shape, finite values, quaternion norm and opening range. Confirmed invalid actions execute zero controls and return structured feedback; they remain recorded. The client may correct them on a later decision.

Counting and termination

Count policy calls, action decisions, rejected actions, acknowledged controls and physics ticks separately. One acknowledged control runs ten native physics ticks at 0.004 seconds. A model may propose a bounded chunk, but the service still executes and acknowledges controls one at a time.

Terminate on native success/end, native horizon, explicit model stop, or a terminal infrastructure/policy error. The 1100-control horizon is a benchmark limit; model-call limits are model/evaluation configuration and must be reported separately. Waiting for a model advances no physics. An uncertain native action is never silently replayed.

Scoring

Success is the pinned native result end_flag[0] and success[0]. Native reward and cached process score are recorded without an extra stateful scorer call. Partial process score is not task success. Report success count over the full selected named-panel denominator (5 for fast, 50 for full), plus native scores and all infrastructure or policy errors. Do not remove failures or replace layouts.

Isolation and evidence

The model communicates only through the authenticated loopback SIM service. The server owns the native environment, schedule and score; the model owns its decision loop. The shared harness may coordinate output and publish a closed report, but it cannot expose server-private fields or generate actions.

Essential results retain the selected schedule and protocol/method configuration, native outcome, executed counters and separate infrastructure/policy errors. Detailed interaction/media retention is optional, default enabled. It does not alter the sensors, controller, horizon, scoring or attempts specified above.

The existing full-evidence audit path additionally uses:

• immutable schedule and protocol/source declarations;
• client receipt and server claim/session record;
• submitted/rejected actions and public observations;
• native events, final result and cleanup receipts;
• head, left-wrist and right-wrist video;
• model transcript/usage when available;
• separate infrastructure and policy errors.

Published results belong under AgentPolicies/<model-profile>/results/robodojo/. Raw evidence may live in durable artifact storage or local ignored barks/. The full-evidence publisher binds its selected raw evidence by hash; the generic basic-result publisher does not require those optional logs, videos or a source audit. Current native workers still capture their own media independently of client retention settings. See recording scope. Never rewrite a closed failure to make a report publishable.

Official deployment: second task stack_bowls

The official deployment additionally accepts the predeclared task stack_bowls under the same RGB/proprio28/EEF16 interface. The historical reference deployment still accepts only classify_objects; its schedule, instruction and horizon are unchanged. Task identity and schedule are part of each episode's frozen metadata.

Official task	Native horizon	Fast panel	Full task panel	Available seed-0 candidates
classify_objects	1100	layouts 0–4	layouts 0–49 (50)	55
stack_bowls	800	layouts 0–4	layouts 0–24 (25)	55

The bowl task's native public language is “Stack the three bowls together.” The public service instruction also requires return to the initial end-effector poses, consistent with the existing direct-control task convention. Native success and process scores remain owned by the pinned task; no scoring logic is replaced.

These values are bound to native revision ee67a1468510da7624a089164402359f2afc72c8: stack_bowls.py declares horizon 800, _task.yml sets teleoperation data and eval_nums: 25, and the unmodified seed manager enumerates exact task-prefixed numbered filenames. Official HF asset revision 91f76c28d93dd20c5fa46ce6a5a1d96a4f384acd contains stack_bowls_0.json through stack_bowls_54.json for seed 0. The 55-file candidate inventory does not enlarge the 25-episode full panel. The service still uses campaign seed 20260907, native and initial simulator seed 0, and one attempt per selected layout.

The worker verifies all candidate file hashes without interpreting reset layout contents, checks the untouched seed-manager mapping, and requires the native teleoperation stabilization flag to be true before reset. New task readiness requires a separately recorded native reset/action/close diagnostic; offline schedule tests alone do not establish renderer or physics readiness. This local EEF16 track is not automatically equivalent to the official leaderboard baseline.