A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is
more severe in mobile manipulation, where collecting demonstrations is harder than in …

Imitation learning from human demonstrations is a powerful framework to teach robots new
skills. However, the performance of the learned policies is bottlenecked by the quality, scale …

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 …

Learning from demonstration is an approach to directly teach robots new tasks without
explicit programming. Prior methods typically collect demonstration data through kinesthetic …

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

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 …

Imitation learning encodes skills through human-friendly demonstration, enabling the
acquisition of complex skills. During the teleoperation-based demonstration process for dual …

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 research, I report on novel methods to afford more intuitive and efficient robot
teleoperation control using human motion. The overall premise of this work is that allowing …

We tackle the problem of developing humanoid loco-manipulation skills with deep imitation
learning. The difficulty of collecting task demonstrations and training policies for humanoids …