5 accepted papers
In this letter, we describe an approach to achieve dynamic legged locomotion on physical robots which combines existing methods for control with reinforcement learning. Specifically, our goal is a control hierarchy in which highest-level behaviors are planned through reduced-order models, which desc
In this work, we use first principles of kinematics to provide a fundamental insight into mechanical power distribution within multi-actuator machines. Individual actuator powersânot their net sumâdetermine the efficiency and actuator size of a multi-joint machine. Net power delivered to the env…
Hybrid zero dynamics (HZD) has emerged as a popular framework for the stable control of bipedal robotic gaits, but typically designing a gait's virtual constraints is a slow and undependable optimization process. To expedite and boost the reliability of HZD gait generation, we borrow methods from tr…
In this paper we propose the fastest converging control policy (also known as deadbeat control) for walking with the bipedal spring-mass model, which serves as an abstraction of a robot on compliant legs. To fully leverage the passive dynamics of the system, the touchdown angle of the swing-leg is a…
Many control policies developed for legged robots are based on control of an underlying, simplified version of the dynamics of the robot. A good example is the Linear Inverted Pendulum Model (LIPM) which has become the standard control template for ZMP-based rigid robots. For compliant robots, this…