5 papers
Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies
Qingyi Chen, Joseph Ruan, Zachary Kingston
Generative robot policies can represent diverse, multimodal behaviors, but adapting pretrained policies to deployment-time constraints such as collision avoidance and orientation m…
Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization
Qingyi Chen, Kevin Zhang, Lucas Chen +1
Collision-free motion planning requires reliable collision models from sensed environments and validation of states along a continuous trajectory. To make this tractable, most plan…
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
Qingyi Chen, Zachary Kingston, Ahmed H. Qureshi
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized plan…
Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning
Qingyi Chen, Ruiqi Ni, Junyoung Kim +1
Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots to navigate dynamic environment…
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
Qingyi Chen, Zachary Kingston, Ahmed H. Qureshi
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized plan…