1 citations · 2 across the 14 of their papers we have counts for
14 papers
Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments
Wei Han Chen, Yuchen Liu, Alexiy Buynitsky +1
Robot navigation in large, complex, and unknown indoor environments is a challenging problem. The existing approaches, such as traditional sampling-based methods, struggle with res…
Manip4Care: Robotic Manipulation of Human Limbs for Solving Assistive Tasks
Yubin Koh, Ahmed H. Qureshi
Enabling robots to grasp and reposition human limbs can significantly enhance their ability to provide assistive care to individuals with severe mobility impairments, particularly…
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 Temporal Difference Metric Learning for Robot Motion Planning
Ruiqi Ni, Zherong Pan, Ahmed H Qureshi
The motion planning problem involves finding a collision-free path from a robot's starting to its target configuration. Recently, self-supervised learning methods have emerged to t…
Integrating Active Sensing and Rearrangement Planning for Efficient Object Retrieval from Unknown, Confined, Cluttered Environments
Junyong Kim, Hanwen Ren, Ahmed H. Qureshi
Retrieving target objects from unknown, confined spaces remains a challenging task that requires integrated, task-driven active sensing and rearrangement planning. Previous approac…
Physics-informed Neural Motion Planning on Constraint Manifolds
Ruiqi Ni, Ahmed H. Qureshi
Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds. These problems appear in…