9 citations · 10 across the 5 of their papers we have counts for
6 papers
RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning
Pablo Ortega-Kral, Eliot Xing, Arthur Bucker +13
Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to showcase these models on differ…
Soft and Compliant Contact-Rich Hair Manipulation and Care
Uksang Yoo, Nathaniel Dennler, Eliot Xing +4
Hair care robots can help address labor shortages in elderly care while enabling those with limited mobility to maintain their hair-related identity. We present MOE-Hair, a soft ro…
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation
Eliot Xing, Vernon Luk, Jean Oh
Recent advances in GPU-based parallel simulation have enabled practitioners to collect large amounts of data and train complex control policies using deep reinforcement learning (R…
How is the Pilot Doing: VTOL Pilot Workload Estimation by Multimodal Machine Learning on Psycho-physiological Signals
Jong Hoon Park, Lawrence Chen, Ian Higgins +11
Vertical take-off and landing (VTOL) aircraft do not require a prolonged runway, thus allowing them to land almost anywhere. In recent years, their flexibility has made them popula…
Self-Activating Neural Ensembles for Continual Reinforcement Learning
Sam Powers, Eliot Xing, Abhinav Gupta
The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intellig…
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents
Sam Powers, Eliot Xing, Eric Kolve +2
Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this…