Learning Generic and Dynamic Locomotion of Humanoids Across Discrete Terrains
We introduce a novel locomotion architecture that integrates a neural network policy, trained through RL in simplified environments, with a state-of-the-art motion controller combining model-predictive control (MPC) and whole-body impulse control (WBIC). The policy efficiently learns high-level locomotion strategies, such as gait selection and step positioning, without the need for full dynamics simulations. This control architecture enables humanoid robots to dynamically navigate discrete terrains, making strategic locomotion decisions (e.g., walking, jumping, and leaping) based on ground height maps. Our results demonstrate that this integrated control architecture achieves dynamic locomotion with significantly fewer training samples than conventional RL-based methods and can be transferred to different humanoid platforms without additional training. The control architecture has been extensively tested in dynamic simulations, accomplishing terrain height-based dynamic locomotion for three different robots.
A Biomechanics-Inspired Approach to Soccer Kicking for Humanoid Robots
Soccer kicking is a complex whole-body motion that requires intricate coordination of various motor actions. To accomplish such dynamic motion in a humanoid robot, the robot needs to simultaneously: 1) transfer high kinetic energy to the kicking leg, 2) maintain balance and stability of the entire body, and 3) manage the impact disturbance from the ball during the kicking moment. Prior studies on robotic soccer kicking often prioritized stability, leading to overly conservative quasi-static motions. In this work, we present a biomechanics-inspired control framework that leverages trajectory optimization and imitation learning to facilitate highly dynamic soccer kicks in humanoid robots. We conducted an in-depth analysis of human soccer kick biomechanics to identify key motion constraints. Based on this understanding, we designed kinodynamically feasible trajectories that are then used as a reference in imitation learning to develop a robust feedback control policy. We demonstrate the effectiveness of our approach through a simulation of an anthropomorphic 25 DoF bipedal humanoid robot, named PresToe, which is equipped with 7 DoF legs, including a unique actuated toe. Using our framework, PresToe can execute dynamic instep kicks, propelling the ball at speeds exceeding 11m/s in full dynamic simulation.