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 (IL) is a powerful paradigm to teach robots to perform manipulation tasks
by allowing them to learn from human demonstrations collected via teleoperation, but has …

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 …

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

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 …

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) …

Imitation Learning has empowered recent advances in learning robotic manipulation tasks
by addressing shortcomings of Reinforcement Learning such as exploration and reward …

We propose to perform imitation learning for dexterous manipulation with multi-finger robot
hand from human demonstrations, and transfer the policy to the real robot hand. We …

Imitation learning from human demonstrations is a promising paradigm for teaching robots
manipulation skills in the real world. However, learning complex long-horizon tasks often …

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 …