From Human Walking to Bipedal Robot Locomotion: Reflex Inspired Compensation on Expected and Unexpected Downsteps

Humans and other biological bipedal walkers are extraordinarily agile and robust. This is especially apparent when certain features of the environment are unknown such as unexpected downsteps (for example, suddenly walking off the side-walk or not expecting the last stair when descending). Humans can negotiate these downsteps ---both expected...

Towards incremental kinesthetic teaching of bipedal walking

The large dimensionality of walking motions is a challenge for robot learning. The human seems designated to assist in this learning process, because of their aptness in walking. This paper presents a step in the investigation how a human can teach a robot a walking-like motion using incremental kinesthetic teaching. This approach lets the human...

Deep Reinforcement Learning for Bipedal Robots

Reinforcement Learning (RL) is a general purpose framework for designing controllers for non-linear systems. It tries to learn a controller (policy) by trial and error. This makes it highly suitable for systems which are difficult to control using conventional control methodologies, such as walking robots. Traditionally, RL has only been...