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haixuantao/
unitree-g1-velocity-v28

Name: unitree-g1-velocity-v28
Availability: InStock

onnx

robotics

reinforcement-learning

humanoid

unitree-g1

apache-2.0

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Unitree G1 (29-DOF) — Velocity Tracking Policy (v28)

RL locomotion policy for the Unitree G1, trained on the zealot trainer (nexus GPU physics, native CUDA). Full 29-DOF body in sim (wrist pitch/yaw welded): the policy commands the 12 leg joints; waist and arms are PD-held, with recorded arm-motion playback during training so the policy tolerates a moving upper body (teleop-friendly).

Snapshots (one folder per training iteration; newest is the default pick):

snapshot	notes
`velocity_v28_iter44000/`	RECOMMENDED — the run's optimum (3-command MuJoCo sweep, all 20 s survival): smoothest at every command (knee >5 Hz power 17.6/4.4/18.9% at 0.4 / 0.7 / turn) AND fastest at speed (12.25 m on the 0.7 m/s command)
`velocity_v28_iter41500/`	smooth (26% >5 Hz at 0.4 m/s); superseded by iter44000
`velocity_v28_iter31350/`	fast at 0.4 m/s (8.70 m) but 69% >5 Hz knee chatter; superseded by iter44000
`velocity_v28_iter12300/`	terrain curriculum at level ~12.8 (26.8% slopes); MuJoCo sim2sim: full 20 s survival, 7.90 m
`velocity_v28_iter7900/`	earlier snapshot at the ~0.10/step reward plateau

The run targets 50,000 iterations; later snapshots will be added.

Files

velocity_v28_iter7900/policy.onnx — actor network (ONNX, 0.68 MB)
- Input: obs [N, 265] float32 — 5-frame history × 53, obs normalizer (clip ±5) baked into the graph
- Output: action [N, 12] float32 — joint-position offsets; q_target = default_pos + 0.5 × action
- Export verified against the float64 numpy reference (max abs err 4.8e-6)
velocity_v28_iter7900/policy.safetensors — raw actor checkpoint
velocity_v28_iter7900/env.yaml — deploy contract: joint order, PD gains, action scale, default pose, obs spec, DR summary

Key deploy notes

Ankle gains are kp 40 / kd 2.0 (the v19+ deploy package), NOT the AGILE table's 20/0.2 — run inference with the gains in env.yaml.
Control at 50 Hz; obs builder reference: zealot/examples/biped/sim2sim_xval.py (note its lag-2 last_action convention).
Commands: vx ∈ [−0.8, 0.8] m/s; trained with standing commands, pushes (0.5 m/s / 0.25 rad/s), motor delay 0–4 substeps, terrain slopes to 26.8%.

Training

Trainer: zealot biped_train_gpu (PPO, mirror augmentation, obs-normalizer freeze), 4096 envs on an RTX 5090, ~2.5 s/iter
Physics: nexus multibody (native CUDA, sm_120), force-based PD motors, force-sensed foot contact (1 N threshold)
Robot: unitree_g1_29dof_agile spec (AGILE actuator parametrization, deploy ankle override)

haixuantao/unitree-g1-velocity-v28

Unitree G1 (29-DOF) — Velocity Tracking Policy (v28)

Files

Key deploy notes

Training

haixuantao/
unitree-g1-velocity-v28