ETHRC-humanoid/humanoid-v1-velocity

Humanoid V1 — Velocity Locomotion Policy

A velocity-tracking locomotion policy for the ETHRC Humanoid V1 robot, trained in Isaac Lab with rsl-rl (PPO) on the ETHRC-humanoidv1-v0 task. The policy maps proprioceptive observations plus a target base-velocity command to joint position targets, and tracks that command while keeping the robot balanced.

Velocity command

The policy is driven by a 3-D base_velocity command [lin_vel_x, lin_vel_y, ang_vel_z]:

Component	Controls	Sign
lin_vel_x	lateral (strafe)	+ = right, − = left
lin_vel_y	forward / backward	+ = forward, − = backward
ang_vel_z	yaw (turn)	+ = turn left

Linear components are in m/s, angular in rad/s.

Capabilities

Measured as the mean over 300 steps under fixed commands:

Command	Achieved	Tracking
Forward (lin_vel_y = 0.5)	~0.36 m/s	~71%
Sidestep (lin_vel_x = ±0.5)	~0.36 m/s	~71%
Turn (ang_vel_z = 0.5)	0.34 rad/s	68%
Diagonal (lin_vel_y = 0.4, lin_vel_x = 0.3)	0.33 / 0.21 m/s	70–82%
Top speed (lin_vel_y = 1.0)	0.71 m/s	71%

The policy is omnidirectional — forward, backward, lateral, turning, and combined commands (translation + yaw together) all work, and it stays upright under disturbances. Tracking undershoots roughly uniformly (~70%), so a practical rule is to command ≈ 1.4× the target speed to reach it. Sustained top forward speed is about 0.7 m/s.

Example rollouts for each command are in videos/.

Files

Path	Description
model_3999.pt	rsl-rl checkpoint (final, iteration 3999)
exported/policy.pt	TorchScript policy (deployment)
exported/policy.onnx (+ .data)	ONNX policy (deployment)
params/agent.yaml, params/env.yaml	training / environment configs
videos/	evaluation clips per command

Usage

Evaluate or record a rollout with Isaac Lab (requires rsl-rl ≥ 5.0):

cd <rc_humanoid_rl_lab>
isaaclab.sh -p scripts/rsl_rl/play.py \
  --task=ETHRC-humanoidv1-v0 --num_envs=4 --headless --video --video_length=300 \
  --checkpoint model_3999.pt \
  env.commands.base_velocity.ranges.lin_vel_y="[0.5, 0.5]" \
  env.commands.base_velocity.ranges.lin_vel_x="[0.0, 0.0]" \
  env.commands.base_velocity.ranges.ang_vel_z="[0.0, 0.0]"

Set lin_vel_y for forward/backward, lin_vel_x for strafing, and ang_vel_z for turning. For deployment, load exported/policy.pt (TorchScript) or exported/policy.onnx (ONNX) — both take the observation vector and return joint position targets.

Training

• Framework: Isaac Lab + rsl-rl (PPO)
• Task: ETHRC-humanoidv1-v0 — velocity-tracking locomotion
• Observations: proprioception + base-velocity command
• Actions: joint position targets
• Curriculum: lin_vel_cmd_levels progressively widens the commanded velocity range as the track_lin_vel_xy reward improves, so the policy is trained on harder commands only once it tracks the easier ones.

Exact hyperparameters and reward terms are in params/agent.yaml and params/env.yaml.