7 papers
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…
Weakly-supervised Learning for Physics-informed Neural Motion Planning via Sparse Roadmap
Ruiqi Ni, Yuchen Liu, Ahmed H. Qureshi
The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer…
Physics-informed Value Learner for Offline Goal-Conditioned Reinforcement Learning
Vittorio Giammarino, Ruiqi Ni, Ahmed H. Qureshi
Offline Goal-Conditioned Reinforcement Learning (GCRL) holds great promise for domains such as autonomous navigation and locomotion, where collecting interactive data is costly and…
Physics-informed Neural Time Fields for Prehensile Object Manipulation
Hanwen Ren, Ruiqi Ni, Ahmed H. Qureshi
Object manipulation skills are necessary for robots operating in various daily-life scenarios, ranging from warehouses to hospitals. They allow the robots to manipulate the given o…
Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments
Yuchen Liu, Alexiy Buynitsky, Ruiqi Ni +1
Physics-informed Neural Motion Planners (PiNMPs) provide a data-efficient framework for solving the Eikonal Partial Differential Equation (PDE) and representing the cost-to-go func…
Physics-informed Neural Mapping and Motion Planning in Unknown Environments
Yuchen Liu, Ruiqi Ni, Ahmed H. Qureshi
Mapping and motion planning are two essential elements of robot intelligence that are interdependent in generating environment maps and navigating around obstacles. The existing ma…