35 citations · 48 across the 10 of their papers we have counts for
16 papers · 1 filter
DOT-Sim: Differentiable Optical Tactile Simulation with Precise Real-to-Sim Physical Calibration
Yang You, Won Kyung Do, Aiden Swann +3
Simulating optical tactile sensors presents significant challenges due to their high deformability and intricate optical properties. To address these issues and enable a physically…
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…
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…
Deformable Cargo Transport in Microgravity with Astrobee
Daniel Morton, Rika Antonova, Brian Coltin +2
We present pyastrobee: a simulation environment and control stack for Astrobee in Python, with an emphasis on cargo manipulation and transport tasks. We also demonstrate preliminar…
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…
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…