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# RoboTwin clean direct-control protocol

Protocol: `robotwin-local-content/clean-fixed-seed/qpos14-rgb320-state14/fast-v3`.
Opt-in renderer compatibility protocol:
`robotwin-local-content/clean-fixed-seed/qpos14-rgb320-state14-oidn233/fast-v4`.
Status: development server adapter; native acceptance requires separate evidence.
Benchmark registration name: `robotwin`.

These versions add a declared robot-state channel to the former RGB-only
`fast-v1` / `fast-v2` tracks. Old frozen results retain their original inputs and
identities; this is not a silent reinterpretation or a claim of score parity.
Native cameras, actions, control timing, task selection and scoring are unchanged.

## Scope and denominator

The retained launcher lists 50 tasks and clean/randomized settings. The inspected
native `script/eval_policy.py` defaults to 100 accepted test episodes per task,
not the `episode_num: 50` collection field in demo_clean. It starts at
`100000 * (1 + seed)`, performs expert `play_once` feasibility screening, skips
unsuccessful seeds, then resets accepted seeds again for the participant.
Language templates may use expert-generated episode information.

This server does not execute that expert/filtering loop. Its explicit fast
development track chooses one task and one unscreened native seed in advance,
then calls `setup_demo` once. Failure, unstable-scene errors and runtime errors
are retained; none trigger another seed or attempt. Native object-generation
sampling within that one seeded scene setup is unchanged. Language is the
task JSON's fixed `full_description`, without expert execution, hidden object
identifiers or adaptive prompt selection.

| Implemented task | Native object variants retained | Native target-control horizon |
| --- | --- | --- |
| `lift_pot` | `060_kitchenpot`, IDs 0 and 1; native seeded sampling | 400 |
| `click_bell` | `050_bell`, IDs 0 and 1; native seeded sampling | 400 |

Only `demo_clean` with native Aloha is exposed. A service has one selected task,
one declared `native_seed` and one attempt; a second open/reset is rejected even
after failure. Full mode, EEF actions, other tasks and arbitrary native config
overrides are unsupported. This denominator is not native-full coverage and is
not directly comparable with expert-filtered scores. A future full track needs
a reviewed native schedule, not success-seeking retries during evaluation.

## Source, runtime and configuration identity

The native local archive has no own Git metadata. Its selected files are pinned
in `environment/native_snapshot.json`, inventory SHA-256
`ab5a73b0482d50d9c83e8702b37e7e7eb10bf7033e03a8fec59b60b708bef08c`.
`native_source_revision` is `null`; the descriptor's required identity string is
`content-inventory-sha256:<digest>`. A parent repository's revision or current
upstream HEAD is not a substitute for this archive.

The deployment helper copies matching source/assets to a new owned root. It
permits exactly native Aloha left/right CuRobo template path expansion; original
config/template hashes, derived bytes, root and manifest hash are recorded.
Other bytes and the file set remain exact. Runtime metadata and the selected
SAPIEN/MPLib/TOPPRA package file inventory are verified before native imports.
Drivers, JIT outputs, all transitive dependencies and loaded memory are not
attested by these checks.

The default `renderer_runtime: bundled_oidn_2_0_1` preserves the original
runtime snapshot and protocol. The explicit `oidn_2_3_3_cuda_v1` selection uses
an owned SAPIEN 3.0.0b1 overlay with the official OIDN 2.3.3 CUDA libraries.
Only the OIDN library directory and two literal paths in SAPIEN's OIDN loader
change; native RoboTwin code/assets, SAPIEN physics/renderer binaries, shaders,
32 samples per pixel, depth 8, camera definitions and scorer remain unchanged.
It does not disable denoising, select OptiX/CPU, or install task-solving code.

The compatibility inventory additionally checks all three `sapien.libs`
binaries, preserved distribution metadata and regenerated `RECORD`, rejects
unlisted overlay files and preloaded native libraries, and checks actual loaded
library paths after import and the first observation. Other dependencies remain
read-only references to the original selected environment; this is not a fully
isolated or transitively pinned environment. Verification receipts are saved
before native import so an initialization failure retains its runtime identity.
See [renderer setup](environment/RENDERING.md) for preparation and acceptance.

OIDN 2.3.3 supports newer CUDA architectures but has different denoiser
implementation/default filter quality from 2.0.1. The compatibility protocol
therefore has a distinct identity: no pixel parity or interchangeable scores
between these tracks are claimed. A device/filter check is not native rendering
validation; separately inspect the declared server diagnostic and all cameras.

## Observation boundary

The actor receives language plus `head_camera`, `left_camera`, `right_camera`
in that order, each native 320 × 240 RGB uint8. There is no flip, resize, crop,
overlay, image annotation or channel conversion. The native camera already
converts its Color buffer to uint8 RGB.

At reset and after every acknowledged control, `state` contains 14 robot values
in the following fixed order, declared individually in `state_names`:

| Indices | Fields | Source and units |
| --- | --- | --- |
| 0–5 | `measured_fl_joint1_rad` … `measured_fl_joint6_rad` | Actual left-arm articulation joint positions, radians |
| 6 | `commanded_left_gripper_01` | Last native normalized left-gripper command; 0 closed, 1 open |
| 7–12 | `measured_fr_joint1_rad` … `measured_fr_joint6_rad` | Actual right-arm articulation joint positions, radians |
| 13 | `commanded_right_gripper_01` | Last native normalized right-gripper command; 0 closed, 1 open |

The pinned native robot's `get_left_arm_real_jointState` and
`get_right_arm_real_jointState` read arm positions through articulation
`get_qpos` in the compiled arm-joint order. Each getter appends its native
gripper command cache. Thus there are **12 measured arm values and two commanded
gripper values**, not 14 measured encoders. Gripper commands do not establish
physical opening, contact or a successful grasp. These read-only getters do not
advance physics or invoke task logic; the state and cameras are sampled at the
same paused control boundary. Measured arm values are never clipped to action
bounds. Missing, non-finite or wrongly shaped state is an explicit error, not
a fabricated zero vector or a fallback to commanded joints.

The similarly named native `joint_action.vector` still contains drive targets;
it is not used as measured state. Only the declared camera arrays and robot
state are projected. Extra front camera, depth, point clouds, segmentation,
calibration, EEF/object poses, seeds, task metadata, native score and debug
channels remain excluded. No arbitrary native observation dictionary is forwarded.

## Direct action boundary

The actor selects this 14-D vector directly:

```text
[fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6, left_gripper,
 fr_joint1, fr_joint2, fr_joint3, fr_joint4, fr_joint5, fr_joint6, right_gripper]
```

Arm entries are absolute native joint radians with authored URDF bounds
`[-10, 10]`. Grippers are absolute normalized commands in `[0, 1]` (0 closed,
1 open), using the native `[-0.01, 0.045]` meter scale. At reset the adapter
checks actual compiled joint names/limits, gripper scale, horizon and physics
timestep. A mismatch is a contract/environment error before actor controls,
not permission to infer different bounds.

One `take_action(vector, action_type="qpos")` executes each control. Native
MPLib TOPP interpolates from previous drive targets to requested absolute joint
targets under fixed low-level velocity/acceleration constraints. It does not
select a grasp or plan around task objects. EEF/CuRobo planning, expert methods,
VLA proposals, fallback task policies and task-solving services are not exposed.

Native setup still constructs planners, moves to homestate, opens grippers and
performs object-stability settling. These are declared initialization, not model
decisions. No `play_once`, expert feasibility check, hidden-state action
generation or scored-seed search occurs.

Native qpos control catches TOPP exceptions/zero-sample paths and uses its fixed
50-tick gripper interpolation for the affected arm. This existing low-level
behavior is preserved and explicitly recorded by passive TOPP instrumentation;
the adapter never substitutes a task action or replays a failed control.
Exceptions outside that native controller retain errors.

## Timing, stopping and success

Physics uses native `1/250`-second ticks. A target consumes a variable number
according to TOPP; `control_hz` is unspecified, not inferred from video FPS.
Native `take_action_cnt` stops at 400 for both tasks, from the pinned
`_eval_step_limit.yml`. Runner decision limits do not extend that horizon.
The example uses 200 policy calls, an action prefix of at most 5 and a separate
1,000-control ceiling.

Native `take_action` checks `check_success` during each physics tick and caches
`eval_success`. The adapter consumes that result without adding a second
stateful scorer query after each target or inferring success from video.
Numeric transition reward only encodes this Boolean success; no shaped reward
is introduced. Success terminates immediately. Native horizon, runner limits,
errors and explicit policy stop can end unsuccessfully. Voluntary persistence
and minimum-decision rules remain policy-owned.

Record calls, executed action decisions, native started target controls,
acknowledged transitions and completed physics ticks separately. A passive
task-side scene view counts physics after reset. Initialization ticks are
unknown, not falsely zero; setup includes settling and fixed controls. On a
native exception, partially executed controls may have consumed physics without
an acknowledgment. Preserve that uncertainty and never replay the action.

## Media, audit and lifecycle

Native events, resolved config/provenance, delivered-frame PNG/RGB hashes, three
named-camera MP4s, counters and cleanup receipts remain evaluator-private under
`AgentPolicies/<profile>/results/robotwin/`. The actor receives only public observations
and execution feedback through its policy interface. Idle wall time does not
advance simulation.

MP4s present one public frame per control at 10 FPS. They are neither real-time
physics captures nor proof of motion between samples. `frames.jsonl` records
completed physics ticks/simulated seconds beside each frame. Include camera
identity and task descriptions in exported paths; distinguish reset-only
diagnostics from model attempts.

Close calls native `close_env` and releases references, retaining errors.
Native gym close does not prove GPU resource reclamation. Use one episode per
server process and stop it after result transfer; do not reuse it for a task.
Logical policy/evaluator boundaries are not an OS sandbox.

## Acceptance gates requiring native evidence

CPU tests validate factory/lifecycle/projection/deployment and passive counters,
not SAPIEN rendering, physics or model performance. Before scoring, independently
review and run a separate one-reset/two-fixed-control infrastructure fixture;
verify compiled limits and delivered cameras, decode videos and audit counts
and cleanup. Keep the fixture out of model success rates. Randomized/full
evaluation and serial/sharded native parity need further implementation and
benchmark-specific evidence.

## Additive official-source deployment track

The explicit `deployment_track: official_ea8b211_sapien303_v1` selects
`robotwin-official-ea8b211/clean-fixed-seed/qpos14-rgb320-state14-sapien303/fast-v1`.
It does not require the historical local archive. The default `local_archive`
track and its original snapshots remain unchanged.

This track pins official [RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin)
commit `ea8b21121ebb3cd201ff5b3fe361944ac94eda3f`; the selected source closure is
listed in `environment/official_source_snapshot.json`. The official evaluator is
`scripts/eval_policy_xpolicylab.py`; configurations live in `env_cfg/task_config`.
Its task classes, measured-state getters, qpos controller, native TOPP behavior,
400-target limits and native success predicates are used without source edits.
The service still selects one unscreened native seed and never invokes the
expert screening/policy-server path of that evaluator. This is a development
track, not an official full-suite score or parity claim with the archived track.

Official assets come from the upstream downloader's Hugging Face dataset
`TianxingChen/RoboTwin2.0`, pinned to revision
`3dc3b798668feb99ac61cc9086d84cbcc3d79186`. The asset receipt binds the downloaded
`embodiments.zip` and `objects.zip` SHA-256 values plus extracted clean-task
files. Only Aloha, bell/pot and required object metadata are copied. Background
textures and unrelated task assets are unnecessary for the declared clean mode.
The 1,547 selected extracted files have canonical inventory SHA-256
`3e0bb720efcf4a516cbaa14af27f50803d33c2ac4124a0bf25f2ebdbde95c3f5`,
fixed in the implementation; a receipt cannot substitute arbitrary file hashes
while merely retaining the two real archive hashes.
Only the two native CuRobo template path expansions are derived during deployment.

The runtime is explicitly Python 3.10.12, SAPIEN 3.0.3, MPLib 0.2.1, TOPPRA 0.6.3,
NumPy 1.26.4 and Torch 2.5.1+cu121. These differ from upstream's installation
recommendations and therefore have a distinct protocol identity. Preparation
binds captured SAPIEN/MPLib/TOPPRA/CuRobo package bytes, including `sapien.libs`
when present; readiness verifies those exact bytes and versions again before
native imports. CuRobo remains required by upstream Aloha initialization even
though actor control uses qpos/MPLib TOPP. No planner fallback is introduced.
The receipt does not attest the GPU driver, every transitive dependency, JIT
outputs or loaded memory. Native acceptance remains the separate one-reset,
two-fixed-control diagnostic, including camera, counter, video and cleanup checks.