Feat/a2 joystick flat#671
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Self-contained robots/a2/ (meshes copied from the A2+Airbot composite), 12 position-style leg actuators with A2-strength gain classes, Go2- compatible IMU/foot sensors. No arm, no keyframe (keyframe lives in the task scene, per the asset/XML structure contract).
The A2 leg-only MJCF mirrors the Go2 joystick sensor/geom/leg-ordering contract and uses <position> actuators, so the task reuses Go2WalkTask unchanged. Only the A2 identity differs: scene path, standing pose, and per-joint PD gains. - A2 flat scene (scene_flat.xml) with four foot-contact sensors and a home keyframe aligned to unitree_rl_mjlab INIT_STATE (height 0.4, thigh 0.9, calf -1.8, hips +-0.1); default_angles derive from it automatically. - A2JoystickCfg / A2JoystickFlatEnv registered and wired into the locomotion registry bootstrap. - Per-joint PD gains via PdControlConfig.position_gains() (hip/thigh kp=100/kd=4, calf kp=150/kd=6) forwarded to the backend; the scalar default keeps Go2 behaviour byte-for-byte unchanged. - a2_joystick_flat/mujoco owner YAML carrying training + reward. - End-to-end contract tests (XML compiles, 12-DOF, obs layout, per-joint model gains, finite step) and support-matrix refresh.
Enable the sim2real DR suite on A2JoystickFlat (budget 500 iters; A2 is ~2.8x Go2 mass) and let the policy stand still at zero command instead of marching in place. Domain randomization: - A2JoystickDomainRandomizationProvider supplies per-joint base gains so randomize_kp/kd scales each actuator off its true baseline (calf off 150, not the shared scalar), and caches the pristine dof-armature + geom-friction tables via the public backend getters (cold path, no feature leak) so randomize_dof_armature and randomize_ground_friction are effective; the floor geom is the priority geom for friction. - com_offset_y/z declared so the base COM randomizes in all three axes. - DR ranges reference unitree_rl_mjlab A2 events (joint_armature scale 0.9-1.1, foot friction 0.3-1.6); body_mass and gravity stay off. Zero-command standing (gated so Go2's default behaviour is unchanged): - RewardConfig.command_threshold gates the phase-driven gait rewards (swing_feet_z / contact) and the gait phase clock: while standing (||command|| <= threshold) the phase freezes per-env and a planted robot earns full contact reward. Default 0.0 keeps Go2's reward vector and phase advance byte-for-byte unchanged. - New rewards stand_still / hip_deviation / stand_feet_air penalize residual micro-stepping and stance drift at zero command, all gated by command_threshold so they cost locomotion nothing. - sample_commands_with_standing is the single source of truth for the standing-aware command distribution, shared by the reset path (DR provider) and mid-episode resampling (resampling_time > 0). Tests: DR-on env constructs and steps finite, friction/armature baselines cached once, floor geom dominates friction; Go2 stand-still contract (phase freeze, gated rewards, command sampling).
TATP-233
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Jul 2, 2026
…ding_fraction Review on #671 flagged that the standing-command logic our A2 PR added to common/commands.py already existed elsewhere. It did: the block "zero a random rel_standing_envs fraction of rows to zero" was copy-pasted inline in six DR provider sites (go1/rough, go2/rough, go2w/rough, go2w/joystick reset+resample, g1/joystick) with no shared helper. Extract the block into common.commands.apply_standing_fraction() (in-place, same RNG guard) and route all six sites plus sample_commands_with_standing through it. Per-site zero_small_xy thresholds (0.08 for rough, 0.2 default for g1/go2w) and heading handling are preserved, and the rough.py resample paths that only do zero_small_xy (no standing fraction) are left untouched. Behaviour-preserving: make test-all still 1431 passed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Summary
加入A2_joystick/mujoco任务
Linked Work
Validation
make checkuv run pytest -m "not slow"Commands actually run:
# paste exact commands hereImpact
mujoco/motrix/ both / noneArtifacts
Checklist