3 papers
cs.RO2024
Any-point Trajectory Modeling for Policy Learning
Chuan Wen, Xingyu Lin, John So +4
Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning. However, the high cost of collec…
cs.RO2023
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks
Xingyu Lin, John So, Sashwat Mahalingam +2
The existing internet-scale image and video datasets cover a wide range of everyday objects and tasks, bringing the potential of learning policies that generalize in diverse scenar…
cs.LG2022
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data
John So, Amber Xie, Sunggoo Jung +5
Autonomous driving is complex, requiring sophisticated 3D scene understanding, localization, mapping, and control. Rather than explicitly modelling and fusing each of these compone…