6 papers
HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
Priya Sundaresan, Rhea Malhotra, Phillip Miao +7
We introduce HoMeR, an imitation learning framework for mobile manipulation that combines whole-body control with hybrid action modes that handle both long-range and fine-grained m…
Mobi-: Mobilizing Your Robot Learning Policy
Jingyun Yang, Isabella Huang, Brandon Vu +3
Learned visuomotor policies are capable of performing increasingly complex manipulation tasks. However, most of these policies are trained on data collected from limited robot posi…
CUPID: Curating Data your Robot Loves with Influence Functions
Christopher Agia, Rohan Sinha, Jingyun Yang +5
In robot imitation learning, policy performance is tightly coupled with the quality and composition of the demonstration data. Yet, developing a precise understanding of how indivi…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning
Jingyun Yang, Zi-ang Cao, Congyue Deng +3
Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem i…
Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress
Christopher Agia, Rohan Sinha, Jingyun Yang +4
Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies a…