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 …

Humans can teleoperate robots to accomplish complex manipulation tasks. Imitation
learning has emerged as a powerful framework that leverages human teleoperated …

Demonstration data plays a key role in learning complex behaviors and training robotic
foundation models. While effective control interfaces exist for static manipulators, data …

Teleoperation is a crucial tool for collecting human demonstrations, but controlling robots
with bimanual dexterous hands remains a challenge. Existing teleoperation systems …

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 …

Dexterous manipulation is a critical area of robotics. In this field, teleoperation faces three
key challenges: user-friendliness for novices, safety assurance, and transferability across …

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 …

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 …