Paper ID: 2410.05681

Whole-Body Dynamic Throwing with Legged Manipulators

Humphrey Munn, Brendan Tidd, David Howard, Marcus Gallagher

Most robotic behaviours focus on either manipulation or locomotion, where tasks that require the integration of both, such as full-body throwing, remain under-explored. Throwing with a robot involves complex coordination between object manipulation and legged locomotion, which is crucial for advanced real-world interactions. This work investigates the challenge of full-body throwing in robotic systems and highlights the advantages of utilising the robot's entire body. We propose a deep reinforcement learning (RL) approach that leverages the robot's body to enhance throwing performance through a strategically designed curriculum to avoid local optima and sparse but informative reward functions to improve policy flexibility. The robot's body learns to generate additional momentum and fine-tune the projectile release velocity. Our full-body method achieves on average 47% greater throwing distance and 34% greater throwing accuracy than the arm alone, across two robot morphologies - an armed quadruped and a humanoid. We also extend our method to optimise robot stability during throws. The learned policy effectively generalises throwing to targets at any 3D point in space within a specified range, which has not previously been achieved and does so with human-level throwing accuracy. We successfully transferred this approach from simulation to a real robot using sim2real techniques, demonstrating its practical viability.

Submitted: Oct 8, 2024