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 …

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 …

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) …

Visual imitation learning provides a framework for learning complex manipulation behaviors
by leveraging human demonstrations. However, current interfaces for imitation such as …

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

Imitation Learning is a promising paradigm for learning complex robot manipulation skills by
reproducing behavior from human demonstrations. However, manipulation tasks often …

We tackle real-world long-horizon robot manipulation tasks through skill discovery. We
present a bottom-up approach to learning a library of reusable skills from unsegmented …

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 …

We present Universal Manipulation Interface (UMI)--a data collection and policy learning
framework that allows direct skill transfer from in-the-wild human demonstrations to …

We address the problem of effectively composing skills to solve sparse-reward tasks in the
real world. Given a set of parameterized skills (such as exerting a force or doing a top grasp …