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cs.RO2024
RAIL: Reachability-Aided Imitation Learning for Safe Policy Execution
Wonsuhk Jung, Dennis Anthony, Utkarsh A. Mishra +4
Imitation learning (IL) has shown great success in learning complex robot manipulation tasks. However, there remains a need for practical safety methods to justify widespread deplo…
cs.RO2024
Guaranteed Reach-Avoid for Black-Box Systems through Narrow Gaps via Neural Network Reachability
Long Kiu Chung, Wonsuhk Jung, Srivatsank Pullabhotla +6
In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's pe…
cs.RO2024
Goal-Reaching Trajectory Design Near Danger with Piecewise Affine Reach-avoid Computation
Long Kiu Chung, Wonsuhk Jung, Chuizheng Kong +1
Autonomous mobile robots must maintain safety, but should not sacrifice performance, leading to the classical reach-avoid problem: find a trajectory that is guaranteed to reach a g…