259 citations · 838 across the 16 of their papers we have counts for
18 papers
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…
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…
Zero-Shot Constrained Motion Planning Transformers Using Learned Sampling Dictionaries
Jacob J. Johnson, Ahmed H. Qureshi, Michael C. Yip
Constrained robot motion planning is a ubiquitous need for robots interacting with everyday environments, but it is a notoriously difficult problem to solve. Many sampled points in…
Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning
Manav Kulshrestha, Ahmed H. Qureshi
Robotic manipulation tasks, such as object rearrangement, play a crucial role in enabling robots to interact with complex and arbitrary environments. Existing work focuses primaril…
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…
Adversarial Imitation via Variational Inverse Reinforcement Learning
Ahmed H. Qureshi, Byron Boots, Michael C. Yip
We consider a problem of learning the reward and policy from expert examples under unknown dynamics. Our proposed method builds on the framework of generative adversarial networks…
Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments
Zaid Tahir, Ahmed H. Qureshi, Yasar Ayaz +1
Rapidly-exploring Random Tree star (RRT*) has recently gained immense popularity in the motion planning community as it provides a probabilistically complete and asymptotically opt…
Motion Planning Networks
Ahmed H. Qureshi, Anthony Simeonov, Mayur J. Bency +1
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffec…
Intrinsically motivated reinforcement learning for human-robot interaction in the real-world
Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa +1
For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limite…
Potential Functions based Sampling Heuristic For Optimal Path Planning
Ahmed Hussain Qureshi, Yasar Ayaz
Rapidly-exploring Random Tree Star(RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal pa…
Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments
Ahmed Hussain Qureshi, Yasar Ayaz
The sampling based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and ef…
Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network
Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa +1
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to com…
Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning
Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa +1
For robots to coexist with humans in a social world like ours, it is crucial that they possess human-like social interaction skills. Programming a robot to possess such skills is a…