DecAP: Decaying Action Priors for Accelerated Learning of Torque-Based Legged Locomotion Policies

Optimal Control for legged robots has gone through a paradigm shift from position-based to torque-based control, owing to the latter's compliant and robust nature. In parallel to this shift, the community has also turned to Deep Reinforcement Learning (DRL) as a promising approach to directly learn locomotion policies for complex real-life tasks. However, most end-to-end DRL approaches still operate in position space, mainly because learning in torque space is often sample-inefficient and does not consistently converge to natural gaits. To address these challenges, we introduce Decaying Action Priors (DecAP), a novel three-stage framework to learn and deploy torque policies for legged locomotion. In the first stage, we generate our own imitation data by training a position policy, eliminating the need for expert knowledge in designing optimal controllers. The second stage incorporates decaying action priors to enhance the exploration of torque-based policies aided by imitation rewards. We show that our approach consistently outperforms imitation learning alone and is significantly robust to the scaling of these rewards. Finally, our third stage facilitates safe sim-to-real transfer by directly deploying our learned torques, alongside low-gain PID control from our trained position policy. We demonstrate the generality of our approach by training torque-based locomotion policies for a biped, a quadruped, and a hexapod robot in simulation, and experimentally demonstrate our learned policies on a quadruped (Unitree Go1).

Multiple Waypoint Navigation in Unknown Indoor Environments

Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these methods often fail to find the optimal balance between computationally inexpensive online path planning, and optimality of the path. Along with this, these works often prove optimality for single-start single-goal worlds. To address these challenges, we present a multiple waypoint path planner and controller stack for navigation in unknown indoor environments where waypoints include the goal along with the intermediary points that the robot must traverse before reaching the goal. Our approach makes use of a global planner (to find the next best waypoint at any instant), a local planner (to plan the path to a specific waypoint), and an adaptive Model Predictive Control strategy (for robust system control and faster maneuvers). We evaluate our algorithm on a set of randomly generated obstacle maps, intermediate waypoints, and start-goal pairs, with results indicating a significant reduction in computational costs, with high accuracies and robust control.