JackBot/ml

JackBot ML training and evaluation

Quick-start

1. Create a Python virtualenv and activate it:

python -m venv .venv
source .venv/bin/activate

2. Install dependencies (GPU users should install torch appropriate for their CUDA):

pip install -r requirements.txt

3. Quick training (short, for smoke test):

python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command

Use multi-robot training with --num-robots and --robot-spacing:

python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --num-robots 3 --robot-spacing 0.75

Choose the start pose at reset with --start-pose:

python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --start-pose init_deg

If you want to watch the agent train in the PyBullet window, add --gui:

python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --gui

4. Evaluate the trained model in GUI mode:

python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 3 --gui

For evaluation with multiple robots and start pose:

python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 3 --gui --num-robots 2 --robot-spacing 0.6 --start-pose init_deg

Notes

• ml/env.py exposes a JackBotEnv gym environment that uses your JackBotUrdf.urdf.
• The observation is [joint_angles..., vx, vy, vz, omega] and the action is per-joint delta in [-1,1].
• Start with small timesteps and in use_gui=False to speed up iteration. Increase timesteps and tune the reward for better walking quality.
• Keep the trained models off hardware until they behave well in simulation.