As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a
learning machine) is trained to perform a task from demonstrations by learning a map** …

Reinforcement learning (RL) provides an appealing formalism for learning control policies
from experience. However, the classic active formulation of RL necessitates a lengthy active …

Recent advance in deep offline reinforcement learning (RL) has made it possible to train
strong robotic agents from offline datasets. However, depending on the quality of the trained …

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 …

Despite the potential of reinforcement learning (RL) for building general-purpose robotic
systems, training RL agents to solve robotics tasks still remains challenging due to the …

Model-free deep reinforcement learning has been shown to exhibit good performance in
domains ranging from video games to simulated robotic manipulation and locomotion …

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 …

Motor neurons are the final common pathway through which the brain controls movement of
the body, forming the basic elements from which all movement is composed. Yet how a …

Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills,
making them an appealing component for general-purpose robotic manipulators. However …