9 citations · 13 across the 19 of their papers we have counts for
35 papers
When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
Adam Labiosa, Josiah P. Hanna
It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning. In this paper, we study the question of…
BIFROST: Bridging Invariant Feature Representation for Observation-space Sim2Real Transfer
Yunfu Deng, Josiah P. Hanna
Sim2real transfer for robot policy learning suffers due to mismatch between simulation and reality. Existing methods typically address each gap in isolation through separate adapta…
Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
Nicholas E. Corrado, Wenyuan Huang, Josiah P. Hanna
Multi-task reinforcement learning (MTRL) aims to train a single agent to efficiently optimize performance across multiple tasks simultaneously. However, jointly optimizing all task…
Abstract Sim2Real through Approximate Information States
Yunfu Deng, Yuhao Li, Josiah P. Hanna
In recent years, reinforcement learning (RL) has shown remarkable success in robotics when a fast and accurate simulator is available for a given task. When using RL and simulation…
Articulated-Body Dynamics Network: Dynamics-Grounded Prior for Robot Learning
Sangwoo Shin, Kunzhao Ren, Xiaobin Xiong +1
Recent work in reinforcement learning has shown that incorporating structural priors for articulated robots, such as link connectivity, into policy networks improves learning effic…
Efficient and Versatile Quadrupedal Skating: Optimal Co-design via Reinforcement Learning and Bayesian Optimization
Hanwen Wang, Zhenlong Fang, Josiah Hanna +1
In this paper, we present a hardware-control co-design approach that enables efficient and versatile roller skating on quadrupedal robots equipped with passive wheels. Passive-whee…