90 citations · 234 across the 17 of their papers we have counts for
4 papers · 1 filter
Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models
Catie Cuan, Allison Okamura, Mohi Khansari
Learning from demonstration is a proven technique to teach robots new skills. Data quality and quantity play a critical role in the performance of models trained using data collect…
Practical Imitation Learning in the Real World via Task Consistency Loss
Mohi Khansari, Daniel Ho, Yuqing Du +6
Recent work in visual end-to-end learning for robotics has shown the promise of imitation learning across a variety of tasks. Such approaches are expensive both because they requir…
Bayesian Imitation Learning for End-to-End Mobile Manipulation
Yuqing Du, Daniel Ho, Alexander A. Alemi +2
In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a…
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
Eric Jang, Alex Irpan, Mohi Khansari +5
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning. We approach th…