6 papers
Adaptive Dynamics Planning for Robot Navigation
Yuanjie Lu, Mingyang Mao, Tong Xu +3
Autonomous robot navigation systems often rely on hierarchical planning, where global planners compute collision-free paths without considering dynamics, and local planners enforce…
Verti-Arena: A Controllable and Standardized Indoor Testbed for Multi-Terrain Off-Road Autonomy
Haiyue Chen, Aniket Datar, Tong Xu +6
Off-road navigation is an important capability for mobile robots deployed in environments that are inaccessible or dangerous to humans, such as disaster response or planetary explo…
CARoL: Context-aware Adaptation for Robot Learning
Zechen Hu, Tong Xu, Xuesu Xiao +1
Using Reinforcement Learning (RL) to learn new robotic tasks from scratch is often inefficient. Leveraging prior knowledge has the potential to significantly enhance learning effic…
Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning
Linji Wang, Tong Xu, Yuanjie Lu +1
Robotics Reinforcement Learning (RL) often relies on carefully engineered auxiliary rewards to supplement sparse primary learning objectives to compensate for the lack of large-sca…
Decremental Dynamics Planning for Robot Navigation
Yuanjie Lu, Tong Xu, Linji Wang +2
Most, if not all, robot navigation systems employ a decomposed planning framework that includes global and local planning. To trade-off onboard computation and plan quality, curren…
Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for Vertically Challenging Terrain
Tong Xu, Chenhui Pan, Madhan B. Rao +4
Recent advancement in off-road autonomy has shown promises in deploying autonomous mobile robots in outdoor off-road environments. Encouraging results have been reported from both…