challenge-inference-protocol

name	challenge-inference-protocol
description	Reference for the Simulation Challenge inference wire protocol — the exact obs (input) and action (output) message format exchanged between the gateway/genie-sim simulator and the contestant's inference agent over the reverse WebSocket tunnel. Covers the JSON-RPC envelope, image encoding, per-board state/action joint layout, the plain-msgpack response caveat, and the authoritative source files. Trigger: When the user asks about "推理接口协议", "inference protocol", "obs/action format", "观测/动作格式", "网关下发什么", "agent 返回什么", "what does the gateway send", "result envelope", "state layout", "动作怎么拆", or needs to implement/debug the obs→model→action adapter in a tunnel agent.
metadata	{"author":"zy","version":"1.0"}

challenge-inference-protocol — Inference wire protocol (obs in / action out)

This documents the application-layer payload the Simulation Challenge gateway (driven by the genie-sim simulator) exchanges with a contestant inference agent. The transport (reverse WebSocket tunnel, control frames, data-frame framing) is specified in the appendix of challenge-run-agent; this skill covers only what goes inside the data-frame payload.

The gateway treats the payload as opaque bytes. The schema below is defined by the genie-sim client, not the gateway. Authoritative source: main/source/geniesim/benchmark/policy/corobotpolicy.py — get_payload() builds the request, _parse_result() / infer() parse the response. Reference agent implementation: ACoT-VLA/scripts/tunnel_agent.py — _adapt_obs() (input), _build_action_response() (output).

---

1. Input — observation (gateway → agent)

A msgpack-encoded JSON-RPC envelope. Decode with msgpack_numpy.unpackb (str keys).

{
  "method": "infer",
  "params": {
    "timestamps": { "head": <ns int>, "states": <ns int> },
    "images": {
      "head":       { "encoding": "JPEG", "image_data": <bytes>, "height": 400,  "width": 640  },
      "hand_left":  { "encoding": "JPEG", "image_data": <bytes>, "height": 1056, "width": 1280 },
           <bytes>     
    
     
                  
          ..    
        ..  
            ..      
    
            
        
         
       
      
      ... 
  

Image decode: each camera is JPEG bytes under image_data. Decode with cv2.imdecode(IMREAD_COLOR) → HWC BGR, then convert to RGB (the model expects RGB HWC uint8). Depth fields (*_depth) exist in the schema but are commented out / not sent today.

Camera rename to the model's expected names: head→top_head, hand_left→hand_left, hand_right→hand_right.

State assembly for the model (matches training state_keys order joint, left_effector, right_effector, waist):

state = concat(arm_joint_states[14], gripper_states[2], waist_joint_states[5])  # = 21 on G2

One assembly works for all boards: configs with include_waist=False (instruction / spatial) mask the trailing waist dims to zero, so the extra 5 dims are harmless; include_waist=True (manip) needs waist in dims 16–20. info.json is absent at inference, so state_indices=None (no remapping) — the agent must hand the model the already-ordered vector.

---

2. Output — action (agent → gateway)

A msgpack-encoded result envelope:

{
  "result": {
    "left_arm":       { "kind": "JOINT_ABS", "values": [[...7], ...H] },
    "right_arm":      { "kind": "JOINT_ABS", "values": [[...7], ...H] },
    "left_effector":  [[...1], ...H],
    "right_effector": [[...1], ...H],
    "waist":          {    .. ...H    
  

• H = action horizon (instruction/spatial: 50, manip: 30). The sim buffers the whole chunk and replans when it drains.
• kind: JOINT_ABS (absolute joint positions, what this model emits) or EEF_ABS (end-effector pose, IK-solved sim-side). left_arm.kind and right_arm.kind must match.
• The sim reads result["result"]; a top-level {"error": "..."} is treated as a fatal server error.

Map a model action chunk acts[H, D] → the envelope:

slice	field
acts[:, 0:7]	left_arm.values
acts[:, 7:14]	right_arm.values
acts[:, 14:15]	left_effector
acts[:, 15:16]	right_effector
acts[:, 16:]	waist.values (only when D > 16)

instruction/spatial emit D=16 (no waist key); manip emits D=21 only on waist tasks (e.g. sorting_packages), else 16.

⚠️ The plain-msgpack caveat (most common output bug)

genie-sim unpacks the response with plain msgpack (raw=False), NOT msgpack_numpy. If you pack numpy arrays they arrive as ext-encoded garbage and np.array(values) breaks. Convert every array to native Python lists (.tolist()) before packing. Packing the list-only dict with either msgpack or msgpack_numpy is fine.

---

3. Per-board model layout

Board (config.board)	train config	ckpt dir	horizon	state/action dims
instruction, robust	pi05_genie_sim_instruction_and_robust_20260526	checkpoints/instruction_and_robust	50	16 (no waist)
spatial	pi05_genie_sim_spatial_20260511	checkpoints/spatial	50	16 (no waist)
manip	pi05_genie_sim_manip_20260526	checkpoints/manipulation	30	21 (include_waist=True)

ACOT_BOARD=<board> ./scripts/tunnel.sh <gpu> <job_uuid> <gateway> selects config+ckpt. The board the agent serves must match the config.board of the submitted job.

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4. Quick checklist when debugging the adapter

• KeyError: 'state' in the policy → you fed raw params to the model; run _adapt_obs first (gateway no longer sends a flat top-level state).
• Sessions open/close rapidly with no stepping, job stuck at 0 → response format wrong (sim can't parse result["result"]); check the envelope keys and the .tolist() caveat.
• Black/garbled images → forgot BGR→RGB, or fed CHW where HWC expected.
• manip arm flailing / waist ignored → waist not included in state (dims 16–20) or not emitted in the action envelope.