Researchers in biomechanics, neuroscience, human–machine interaction and other fields
are interested in inferring human intentions and objectives from observed actions. The …

This work describes the development of a robotic system that acquires knowledge
incrementally through human interaction where new objects and motions are taught on the …

We present an approach to learn general robot manipulation priors from 3D hand-object
interaction trajectories. We build a framework to use in-the-wild videos to generate …

This work presents the dual benefit of integrating imitation learning techniques, based on the
dynamical systems formalism, with the visual servoing paradigm. On the one hand …

We consider the problem of visual imitation learning without human kinesthetic teaching or
teleoperation, nor access to an interactive reinforcement learning training environment. We …

This paper presents a deep Inverse Reinforcement Learning (IRL) framework that can learn
an a priori unknown number of nonlinear reward functions from unlabeled experts' …

We introduce a One-Shot Learning system where a robot effectively learns how to
manipulate objects by relying solely on the object's name, a single image, and a visual …

We study the problem of learning manipulation skills from human demonstration video by
inferring the association relationships between geometric features. Motivation for this work …

Designing adaptable control laws that can transfer between different robots is a challenge
because of kinematic and dynamic differences, as well as in scenarios where external …

We consider real-world reinforcement learning (RL) of robotic manipulation tasks that
involve both visuomotor skills and contact-rich skills. We aim to train a policy that maps …