6 papers
Generalists Act, Specialists Intervene: Modular Stage-Selective Reinforcement Learning for Vision-Language-Action Manipulation
Chongyu Zhu, Jaden Hinds, Hyegang Kim +3
Vision-language-action (VLA) models often struggle in the precision-critical phases of multi-stage manipulation tasks. To mitigate this issue, VLA models can be used in conjunction…
SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception
Gabe Everett, Brice Gunter, Ryan Vander Stelt +3
Deep reinforcement policy learning directly in physical robots (on-robot learning) remains bottlenecked by slow wall-clock training times. We present SymmGrid, a trajectory level a…
Deep Reinforcement Learning and The Tale of Two Temporal Difference Errors
Juan Sebastian Rojas, Chi-Guhn Lee
The temporal difference (TD) error was first formalized in Sutton (1988), where it was first characterized as the difference between temporally successive predictions, and later, i…
Thermodynamics of Reinforcement Learning Curricula
Jacob Adamczyk, Juan Sebastian Rojas, Rahul V. Kulkarni
Connections between statistical mechanics and machine learning have repeatedly proven fruitful, providing insight into optimization, generalization, and representation learning. In…
Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation
Hongbin Lin, Juan Rojas, Kwok Wai Samuel Au
Sampling efficiency is critical for deploying visuomotor learning in real-world robotic manipulation. While task symmetry has emerged as a promising inductive bias to improve effic…
World Models for General Surgical Grasping
Hongbin Lin, Bin Li, Chun Wai Wong +3
Intelligent vision control systems for surgical robots should adapt to unknown and diverse objects while being robust to system disturbances. Previous methods did not meet these re…