Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

One of the key arguments for building robots that have similar form factors to human beings
is that we can leverage the massive human data for training. Yet, doing so has remained …

Legged robots are designed to perform highly dynamic motions. However, it remains
challenging for users to retarget expressive motions onto these complex systems. In this …

We propose a structured prediction approach for robot imitation learning from
demonstrations. Among various tools for robot imitation learning, supervised learning has …

This paper introduces a novel deep-learning approach for human-to-robot motion
retargeting, enabling robots to mimic human poses accurately. Contrary to prior deep …

This paper enables real-world humanoid robots to maintain stability while performing
expressive motions like humans do. We propose ExBody2, a generalized whole-body …

This paper addresses the critical need for refining robot motions that, despite achieving a
high visual similarity through human-to-humanoid retargeting methods, fall short of practical …

For humanoids to be deployed in demanding situations, such as search and rescue, highly
intelligent decision making and proficient sensorimotor skill is expected. A promising …

Whole-body motion imitation has gained wide attention in recent years as it can enhance the
locomotive capabilities of humanoid robots. In this task, non-intrusive human motion …

We present a novel method for collaborative robots (cobots) to learn manipulation tasks and
perform them in a human-like manner. Our method falls under the learn-from-observation …