This paper presents a data-driven control design framework to achieve robust tracking control without exploiting mathematical model of nonlinear underactuated mechanical systems (UMS). The method leverages the differential flatness property of linearized systems and online estimation and compensation of disturbances by active disturbance rejecti…
Underactuated double-pendulum (acrobot) on a cart to play with direct optimal control methods
Benchmarking SAC, TD3, and DDPG on multi-skill bipedal locomotion using progressive waypoint-based reward shaping in PyBullet. Published at IEEE ICC 2025.
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
Assignment Codes for the MIT OCW Underactuated Robotics and the Robotics Course at IITM
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