259 citations · 297 across the 5 of their papers we have counts for
12 papers
MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints
Linjun Li, Yinglong Miao, Ahmed H. Qureshi +1
Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and those tha…
Constrained Motion Planning Networks X
Ahmed H. Qureshi, Jiangeng Dong, Asfiya Baig +1
Constrained motion planning is a challenging field of research, aiming for computationally efficient methods that can find a collision-free path on the constraint manifolds between…
Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots
Jacob J. Johnson, Linjun Li, Fei Liu +2
Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints be…
Neural Manipulation Planning on Constraint Manifolds
Ahmed H. Qureshi, Jiangeng Dong, Austin Choe +1
The presence of task constraints imposes a significant challenge to motion planning. Despite all recent advancements, existing algorithms are still computationally expensive for mo…
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
Ahmed H. Qureshi, Yinglong Miao, Anthony Simeonov +1
This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks t…
Composing Task-Agnostic Policies with Deep Reinforcement Learning
Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin +3
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines. To date, there has been plenty…