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