Biological systems, including human beings, have the innate ability to perform complex
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

Learning new motor tasks from physical interactions is an important goal for both robotics
and machine learning. However, when moving beyond basic skills, most monolithic machine …

We provide a general approach for learning robotic motor skills from human demonstration.
To represent an observed movement, a non-linear differential equation is learned such that …

Many motor skills in humanoid robotics can be learned using parametrized motor primitives
as done in imitation learning. However, most interesting motor learning problems are high …

Acquisition of new sensorimotor knowledge by imitation is a promising paradigm for robot
learning. To be effective, action learning should not be limited to direct replication of …

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …

We consider an imitation learning approach to model robot point-to-point (also known as
discrete or reaching) movements with a set of autonomous Dynamical Systems (DS). Each …

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical
Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed …

We introduce the Riemannian Motion Policy (RMP), a new mathematical object for modular
motion generation. An RMP is a second-order dynamical system (acceleration field or …