dropbear-locomotion / docs /POSE_TRANSFER.md
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Release Dropbear locomotion v0.1.0 (model_8745)
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Pose-transfer adapter

The repository includes two complementary mechanisms for teaching semantic motion while respecting the Dropbear articulation.

Playback constraint projection

dropbear_walk/pose_guidance.py defines named stand and crouch poses and a test sequence:

stand -> walk with arm swing -> crouch -> stand up -> walk

ConstrainedPoseGuide maps named semantic targets into the 14-dimensional policy action order, clamps targets to softened mechanical joint limits, and rate-limits guided joints below their configured actuator velocity limits. During walking, the policy retains leg ownership and the adapter guides only reciprocal shoulder swing.

This is useful for visualization, mechanical-range discovery, and generating curriculum references. It is not a substitute for a learned balance policy.

Learned residual pose control

The environment also supports pose-reference observations and reward terms for joint pose, COM height, and height-trajectory velocity. The training wrapper can:

  • append pose/COM feedback channels to older checkpoints;
  • zero-initialize added network columns to preserve inherited behavior;
  • train a policy residual around a semantic pose;
  • bias pelvis pitch, knees, and ankle joints by name;
  • stagger pose phases across vectorized environments;
  • evaluate stand/crouch phases and report joint/height errors.

Relevant flags begin with --pose-*, --com-*, --adapt-pose-checkpoint, and --calibrate-*-normalizer.

What v0.1.0 does and does not claim

The included model_8745.pt is the validated upright planar locomotion checkpoint. The pose-transfer stack is included and has been exercised in simulation, but crouched locomotion and floor-to-stand recovery are not release-qualified policies in v0.1.0.

Recommended progression:

  1. hold zero velocity while lowering COM height smoothly;
  2. recover to standing at zero velocity;
  3. add low-speed forward/back/left/right commands at fixed crouch height;
  4. randomize target height within mechanically reachable bounds;
  5. train ground recovery separately with staged initial poses, contact-aware rewards, and strict actuator/impact limits;
  6. combine skills only after each policy passes deterministic transition gates.