underactuated-biped

This project presents a robust and energy-efficient obstacle avoidance framework for an 8-DOF bipedal robot using Deep Reinforcement Learning (Soft Actor-Critic). By tightly integrating an A* planner with a responsive local control policy, the framework successfully navigates through densely cluttered environments while maintaining stability, minim

Benchmarking SAC, TD3, and DDPG on multi-skill bipedal locomotion using progressive waypoint-based reward shaping in PyBullet. Published at IEEE ICC 2025.