13 papers
PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data
Aishwarya Mandyam, Jason Meng, Ge Gao +4
Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment. Recent advances have shown that leveraging auxiliary dataset…
Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Maggie Wang, Stephen Tian, Aiden Swann +3
Learning robotic manipulation policies directly in the real world can be expensive and time-consuming. While reinforcement learning (RL) policies trained in simulation present a sc…
Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturing
Kyle Brown, Dylan M. Asmar, Mac Schwager +1
Mobile autonomous robots have the potential to revolutionize manufacturing processes. However, employing large robot fleets in manufacturing requires addressing challenges includin…
ARCH: Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly
Jiankai Sun, Aidan Curtis, Yang You +8
Generalizable long-horizon robotic assembly requires reasoning at multiple levels of abstraction. While end-to-end imitation learning (IL) is a promising approach, it typically req…
Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
Ruiyang Hao, Haibao Yu, Jiaru Zhong +16
With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing dri…
Safe, Out-of-Distribution-Adaptive MPC with Conformalized Neural Network Ensembles
Jose Leopoldo Contreras, Ola Shorinwa, Mac Schwager
We present SODA-MPC, a Safe, Out-of-Distribution-Adaptive Model Predictive Control algorithm, which uses an ensemble of learned models for prediction, with a runtime monitor to fla…