While imitation learning methods have seen a resurgent interest for robotic manipulation, the
well-known problem of compounding errors continues to afflict behavioral cloning (BC) …

We present a deep imitation learning framework for robotic bimanual manipulation in a
continuous state-action space. A core challenge is to generalize the manipulation skills to …

Billions of people use chopsticks, a simple yet versatile tool, for fine manipulation of
everyday objects. The small, curved, and slippery tips of chopsticks pose a challenge for …

Optimizing behaviors for dexterous manipulation has been a longstanding challenge in
robotics, with a variety of methods from model-based control to model-free reinforcement …

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously
difficult for robots because they require precision, careful coordination of contact forces, and …

Learning from demonstration holds the promise of enabling robots to learn diverse actions
from expert experience. In contrast to learning from observation-action pairs, humans learn …

Learning dexterous manipulation in high-dimensional state-action spaces is an important
open challenge with exploration presenting a major bottleneck. Although in many cases the …

In mobile manipulation (MM), robots can both navigate within and interact with their
environment and are thus able to complete many more tasks than robots only capable of …

In this paper, we discuss a framework for teaching bimanual manipulation tasks by imitation.
To this end, we present a system and algorithms for learning compliant and contact-rich …

We introduce a simple new method for visual imitation learning, which allows a novel robot
manipulation task to be learned from a single human demonstration, without requiring any …