I have working MuJoCo models of a customized Unitree Go2, where the CALF geometry has been structurally changed, stretched, and re-balanced. The walking and trot gaits are based on the original Go2, but the CALF structure has been modified, including changes in bending direction and joint angles. In particular, the front-leg CALF has a completely inverted structure compared to the original Go2, bending inward instead of outward. In addition, basic sit-to-stand and stand-to-sit motions must be implemented as a prerequisite, since stable transitions into and out of locomotion are essential. Although walking and trot behaviors already run, the robot still wobbles, tips over, or drifts after a few meters. My priority is to push these gaits through a full reinforcement-learning training cycle until they remain rock-steady on uneven virtual terrain. Where things stand • Joint limits and contact parameters are roughly in place, yet the trade-off between stability and freedom is still delicate—too loose and the legs splay, too tight and the RL agent plateaus. • Dynamics have been validated against CAD mass properties, but I’m open to a second pair of eyes if you spot inconsistencies. • Training is done with MuJoCo + Python (mujoco-py), scene_terrain.xml What I need from you 1. Review and refine the joint-limit definitions so the agent can safely explore without sacrificing natural stride length. 2. Design or tweak a reward function that explicitly favours stability over long horizons for walking and trot. 3. Run or script RL experiments (preferably on GPU) until each gait reaches consistent forward velocity ≥ 0.8 m/s with less than 3 cm lateral drift over 10 m. 4. Hand back clean, commented code plus a short report (metrics, screenshots or brief demo video). Tools you are comfortable with—MuJoCo, RLlib, Stable-Baselines, Isaac Gym, JAX—are all welcome as long as the final solution plugs into my current Python workflow. Acceptance check • Both gaits complete 50-m virtual runs on flat and mild 15° slopes without falls. • Re-running training from scratch with the supplied scripts reproduces ±5 % of your reported reward curve. • Joint torques stay within manufacturer specs. If this sounds like your kind of challenge, let’s stabilise this robot together. https://github.com/felixokolo/go2_gait_planner https://github.com/unitreerobotics/unitree_mujoco/tree/main/unitree_robots/go2 https://www.youtube.com/shorts/bbROvIr_dDw trot animation https://www.youtube.com/shorts/I--IpZ2Wjxs walk animation