Dropbear Locomotion

COM-guided humanoid locomotion for the Dropbear robot in NVIDIA Isaac Lab, trained with RSL-RL PPO. This repository contains the robot USD, complete training environment, reward and penalty stack, checkpoint adapters, deterministic evaluator, interactive live viewer, and the validated v0.1.0 policy artifacts. The experimental v0.2.0 update adds terrain-height observations, low-obstacle curriculum training, stronger bilateral gait shaping, and real training-environment passthrough in the live viewer.

The frame above is an actual vectorized training environment mirrored from the headless trainer, not an independent checkpoint rollout. TRAIN LIVE • DR streams its complete robot state, command, terrain level, randomized mass, and friction to the split-GPU renderer. Click the button to restore the saved manual rollout and joystick control.

Training lineage and credits

The original Dropbear policy weights through approximately training iteration 7000 come from Hyperspawn Robotics. The newer continuation layers—including COM-guided training, gait and contact shaping, checkpoint adapters, deterministic evaluation, and live-view tooling—are co-authored with robit-man. Checkpoint iteration numbers preserve that continuous training lineage.

Experimental v0.2.0 obstacle checkpoint

model_10479.pt is the current Stage 65b checkpoint. It expands the actor and critic from 58/72 to 162/176 inputs by appending a 104-value terrain-height scan. The new columns were zero-initialized when branching from model_10280.pt, preserving the source policy exactly before obstacle fine-tuning.

Stage 65b used 512 parallel environments, a fixed +0.20 m/s forward command, disabled pushes, and a curriculum containing 35% flat retention tiles and 65% anchored cross-path boxes. Full box height spans 3–14 cm; the terrain curriculum promotes successful environments through the difficulty rows.

Final training signal at iteration 10479	Value
Normal time-out fraction	84.46%
Bad-orientation termination	15.54%
Feet-overlap termination	0.00%
Mean episode length	736.2 / 1,000 steps
Mean terrain level	2.57
Planar velocity error	0.258 m/s

This checkpoint is published for continued terrain and gait training. It is not marked converged: the last 20 updates had an 85.4% mean horizon and the reward curve had not plateaued. The v0.1.0 checkpoint below remains the deterministically validated flat-ground release.

Directional obstacle continuation

Stage 66 continues model_10479.pt across forward, backward, robot-left, robot-right, turn-left, and turn-right box approaches. The 3–14 cm obstacle is rotated and placed on the matching approach path; flat retention environments cycle through the same six command modes. Knee and step-through shaping use equal left/right weights. Mass and contact friction remain randomized, while impulse disturbances are deferred until this first directional gate is stable.

Reproduce the branch with:

DROPBEAR_TRAIN_GPU=0 bash scripts/train_stage66_directional_obstacles.sh

For synchronized three-A100 training, expose the three physical device indices as a comma-separated list and set the worker count:

DROPBEAR_TRAIN_GPU=0,1,2 \
DROPBEAR_TRAIN_GPU_COUNT=3 \
bash scripts/train_stage66_directional_obstacles.sh

Boxes isolate single clearance and landing failures. The intended progression after this gate is curb-style step-up/down, then repeated stairs; stairs are not mixed into the initial branch so bilateral regressions remain attributable.

v0.2.1 live-viewer fix

Manual control can remain pinned to the validated Stage 65b actor while the upper-right metrics and TRAIN LIVE mode follow the active Stage 66 run. This prevents a newly written training checkpoint from replacing the manual actor mid-episode:

CUDA_VISIBLE_DEVICES=1 env_isaaclab/bin/python -u \
  scripts/play_dropbear_live.py \
  --actor-base-lin-vel \
  --viewer-com-control \
  --viewer-directional-obstacle-terrain \
  --viewer-disable-pushes \
  --task Isaac-Dropbear-Velocity-Play-v0 \
  --checkpoint checkpoints/dropbear_locomotion_v0.2.0/model_10479.pt \
  --follow-checkpoints logs/rsl_rl/dropbear_velocity/STAGE66_RUN \
  --pin-manual-policy \
  --training-live-state /tmp/dropbear_training_live.json \
  --viewer-command 0.20 \
  --viewer-fps 50 \
  --viewer-display-fps 50 \
  --device cuda:0 \
  --num_envs 1 \
  --viz none

The simulation and RTX render stay on the selected CUDA device. The desktop window presents consecutive policy frames and uses optical flow only between two genuine rendered frames; scene resets are hard cuts.

v0.1.0 checkpoint

model_8745.pt is the frozen Stage 46 checkpoint. It uses a 58-dimensional actor observation, a 72-dimensional privileged critic observation, and 14 joint-position actions. The actor includes measured base velocity for closed-loop correction and two COM-reference error channels.

Deterministic evaluation used the full 20-second/1,000-step horizon, exact commands, a plane, disabled pushes, nominal policy-joint reset, and strict inter-foot gates of 0.16 m center distance and 0.10 m anatomical lateral separation.

Command	Trials	Commanded	Mean COM velocity	Mean COM error
Forward	32	`(+0.20, 0.00)`	`(+0.209, +0.014)`	0.094 m/s
Backward	32	`(-0.20, 0.00)`	`(-0.148, +0.005)`	0.119 m/s
Left	32	`(0.00, +0.20)`	`(+0.038, +0.163)`	0.097 m/s
Right	32	`(0.00, -0.20)`	`(+0.004, -0.119)`	0.149 m/s

All 128 cardinal trials timed out normally with zero falls. Forward tracking is already close to the requested speed. Backward and lateral tracking are stable but conservative, especially robot-right; these are honest targets for the next multi-command consolidation pass. Full machine-readable results are in evaluation/cardinal_0.20_strict_128.json.

Included artifacts

Path	Purpose
`checkpoints/dropbear_locomotion_v0.2.0/model_10479.pt`	Experimental terrain-scan/low-obstacle RSL-RL checkpoint
`checkpoints/dropbear_locomotion_v0.2.0/terrain_scan_branch_parent_model_10280.pt`	Exact zero-initialized terrain-scan branch parent
`configs/release_v0.2.0/`	Resolved Stage 65b RSL-RL/environment configs
`training/stage65b/`	Stage 65b TensorBoard event history
`checkpoints/dropbear_locomotion_v0.1.0/model_8745.pt`	Full RSL-RL checkpoint: actor, critic, normalizers, optimizer state
`checkpoints/dropbear_locomotion_v0.1.0/policy.pt`	Exported TorchScript actor
`checkpoints/dropbear_locomotion_v0.1.0/policy.onnx*`	Exported ONNX actor and external weights
`checkpoints/dropbear_locomotion_v0.1.0/warm_start_model_8666.pt`	Exact Stage 45 parent used for the final continuation
`dropbear_walk/isaaclab_asset/dropbear.usd`	Robot articulation and visual/collision asset
`configs/release_v0.1.0/`	Resolved RSL-RL and environment configs
`training/stage46/`	TensorBoard event history
`evaluation/`	Deterministic validation reports

Use model_8745.pt for Isaac Lab playback or continued training. Use policy.pt or policy.onnx for deployment integration after reproducing the same 58-value observation order, normalization, 14-action order, action scale, and control rate described in docs/CHECKPOINTS.md.

Quick start

The release was tested with Isaac Sim 6.0.0, the Isaac Lab repository at commit a4a7602f29e755e2673fe0022ea35566df6dd7d5, Isaac Lab Python package 4.5.22, RSL-RL 5.0.1, PyTorch 2.10.0+cu128, and Python 3.12.

git clone https://github.com/robit-man/dropbear-locomotion.git
cd dropbear-locomotion
git lfs install
git lfs pull

git clone https://github.com/isaac-sim/IsaacLab.git
git -C IsaacLab checkout a4a7602f29e755e2673fe0022ea35566df6dd7d5

# Install Isaac Lab/Isaac Sim according to the upstream instructions, then:
./IsaacLab/isaaclab.sh -p -m pip install -e ./dropbear_walk

Open the interactive live viewer:

./IsaacLab/isaaclab.sh -p scripts/play_dropbear_live.py \
  --actor-base-lin-vel \
  --viewer-com-control \
  --viewer-gait-period 0.55 \
  --viewer-com-stand-height 1.052 \
  --viewer-com-height-delta 0 \
  --viewer-com-height-error-scale 0.03 \
  --viewer-com-vertical-velocity-error-scale 0.08 \
  --viewer-plane \
  --viewer-disable-pushes \
  --viewer-reset-policy-joints-only \
  --viewer-reset-joint-position-range 1.0 1.0 \
  --viewer-reset-joint-velocity-range 0.0 0.0 \
  --task Isaac-Dropbear-Velocity-Play-v0 \
  --checkpoint checkpoints/dropbear_locomotion_v0.1.0/model_8745.pt \
  --viewer-command 0.0 \
  --viewer-lateral-speed 0.20 \
  --viewer-yaw-rate 0.0 \
  --viewer-joystick-max-speed 1.0 \
  --viewer-joystick-max-yaw-rate 1.0 \
  --device cuda:0 \
  --num_envs 1 \
  --viz none \
  --real-time

The first joystick movement takes ownership of the planar command. Up is robot forward, left is robot-left, right is robot-right, down is robot-backward, and release springs to zero. At the pad rim, the left/right sectors add yaw toward the commanded side while retaining strafe; the top/bottom sectors remain straight. Drag anywhere outside the pad to orbit the camera.

The viewer keeps native 960×540 renders at the 50 Hz policy cadence. The display-attached GPU presents the completed RGB frames and desktop controls. Simulation, render, and display hardware names are detected at runtime rather than hard-coded.

For a live trainer/viewer split, add --live-preview-state /tmp/dropbear_training_live.json to scripts/train_dropbear.py and --training-live-state /tmp/dropbear_training_live.json to the viewer. The button beside the joystick then switches between manual control and a sampled environment from the running vectorized trainer. See docs/LIVE_VIEWER.md for the complete commands and state contract.

Documentation

Scope and safety

This is a simulation research release. It is not a hardware safety controller. The v0.1 checkpoint is validated only in the conditions reported above. The v0.2 terrain checkpoint is explicitly experimental. Push robustness, 0.5+ m/s motion, crouched locomotion, and ground-to-stand recovery require additional training and verification before physical use.

License

Code, policy weights, media, and the supplied USD asset are released under CC BY-NC-SA 4.0.

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