Neural Rearrangement Planning for Object Retrieval from Confined Spaces Perceivable by Robot’s In-hand RGB-D Sensor
Hanwen Ren and Ahmed H. Qureshi
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2024. [arXiv (coming soon)]

Physics-informed Neural Motion Planning on Constraint Manifolds
Ruiqi Ni and Ahmed H. Qureshi
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2024. [arXiv (coming soon)]

Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies
Daniel Lawson and Ahmed H. Qureshi
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2024. [arXiv]

Language-guided Active Sensing of Confined, Cluttered Environments via Object Rearrangement Planning
Weihan (Daniel) Chen, Hanwen Ren, and Ahmed H. Qureshi
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2024. [arXiv (coming soon)]

SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving
Vidyaa Krishnan Nivash and Ahmed H. Qureshi
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2024. [arXiv]

Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
Zikang Xiong, Daniel Lawson, Joe Kurian Eappen, Ahmed H. Qureshi, and Suresh Jagannathan
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2024. [arXiv]

Zero-Shot Constrained Motion Planning Transformers Using Learned Sampling Dictionaries
Jacob Johnson, Ahmed H. Qureshi, and Michael C. Yip
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2024. [arXiv]

Evolution-based Shape and Behavior Co-design of Virtual Agents
Zhiquan Wang, Bedrich Benes, Ahmed H. Qureshi, and Christos Mousas
IEEE Transactions on Visualization and Computer Graphics, 2024. [paper]

MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments
Vivek Gupta, Praphpreet Dhir, Jeegn Dani, and Ahmed H. Qureshi
IEEE Robotics and Automation Letters, 2023. [To be presented at ICRA'24] [arXiv]

DeRi-Bot: Learning to Collaboratively Manipulate Rigid Objects via Deformable Objects
Zixing Wang and Ahmed H. Qureshi
IEEE Robotics and Automation Letters, 2023. [To be presented at ICRA'24] [arXiv][video]

Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning
Manav Kulshrestha and Ahmed H. Qureshi
Conference on Robot Learning (CoRL), 2023. [arXiv]

Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformers
Jacob Johnson, Ahmed H. Qureshi, and Michael Yip
IEEE Robotics and Automation Letters, 2023. [arXiv]

Progressive Learning for Physics-informed Neural Motion Planning
Ruiqi Ni and Ahmed H. Qureshi
Robotics: Science and Systems (RSS), 2023. [arXiv]

Robot Active Neural Sensing and Planning in Unknown Cluttered Environments
Hanwen Ren and Ahmed H. Qureshi
IEEE Transactions on Robotics, 2023. [arXiv]

Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling
Daniel Lawson and Ahmed H. Qureshi
IEEE/RSJ International Conference on Intelligent Robot and Systems (IROS), 2023. [arXiv]

Efficient Q-Learning over Visit Frequency Maps for Multi-agent Exploration of Unknown Environments
Xuyang Chen, Ashvin Iyer, Zixing Wang, and Ahmed H. Qureshi
IEEE/RSJ International Conference on Intelligent Robot and Systems (IROS), 2023. [arXiv]

NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
Ruiqi Ni and Ahmed H. Qureshi
International Conference on Representation Learning (ICLR), 2023. [SPOTLIGHT] [arXiv] [openreview]

CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration
Abhinav K. Keshari, Hanwen Ren, and Ahmed H. Qureshi
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2023. [arXiv][video]

Model-free Neural Lyapunov Control for Safe Robot Navigation
Z.Xiong, J.Eappen, A.H.Qureshi, and S.Jagannathan
IEEE/RSJ International Conference on Intelligent Robot and Systems (IROS), 2022. [arXiv]

Motion Planning Transformers: One Model to Plan Them All
J.J.Johnson, L.Li, A.H.Qureshi, and M.C.Yip
arXiv:2106.02791 [cs.RO] [arXiv]

NeRP: Neural Rearrangement Planning for Unknown Objects
A.H.Qureshi, A.Mousavian, C.Paxton, M.C.Yip, and D.Fox
Robotics: Science and Systems, 2021. [paper] [arXiv] [project]

Constrained Motion Planning Networks X
A.H.Qureshi, J.Dong, A.Baig, and M.C.Yip
IEEE Transactions on Robotics, 2021. [paper] [arXiv] [project]

MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints
L.Li, Y.Miao, A.H.Qureshi, and M.C.Yip
IEEE Robotics and Automation Letters, 2021. [paper] [arXiv] [project]

Neural Manipulation Planning on Constraint Manifolds
A.H.Qureshi, J.Dong, A.Choe, and M.C.Yip
IEEE Robotics and Automation Letters, 2020. [paper] [arXiv] [project]

Composing Task-Agnostic Policies via Deep Reinforcement Learning
A.H.Qureshi, J.J.Johnson, Y.Qin, T.West, B.Boots, and M.C.Yip
International Conference on Representation Learning (ICLR), 2020. [paper] [arXiv] [project]

Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots
J.Johnson, L.Li, F.Liu, A.H.Qureshi, and M.C.Yip
IEEE/RSJ International Conference on Intelligent Robot and Systems (IROS), 2020. [paper] [arXiv] [project]

Active Continual Learning for Planning and Navigation
A.H.Qureshi, Y.Miao, and M.C.Yip
ICML Workshop on Real World Experiment Design and Active Learning, 2020

Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
A.H.Qureshi, Y.Miao, A.Simeonov, and M.C.Yip
IEEE Transactions on Robotics, 2020. [paper] [arXiv] [project]

Adversarial Imitation Via Variational Inverse Reinforcement Learning
A.H.Qureshi, B. Boots, and M.C.Yip
International Conference on Representation Learning (ICLR), 2019. [paper] [arXiv] [project]

Motion Planning Networks
A.H.Qureshi, A.Simeonov, M.J.Bency, and M.C.Yip
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2019. [paper] [arXiv] [project]

Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
M.J.Bency, A.H.Qureshi, and M.C.Yip
IEEE/RSJ International Conference on Intelligent Robot and Systems (IROS), 2019. [paper] [arXiv] [project]

Machine Learning based Fixed-Time Optimal Path Generation
M.C.Yip, M.J.Bency, and A.H.Qureshi
US Patent App. 16/222,706, 2019. [paper]

Intrinsically motivated reinforcement learning for human–robot interaction in the real-world
A.H.Qureshi, Y.Nakamura, Y.Yoshikawa, and H.Ishiguro
Neural Networks, 2018. [paper] [arXiv]

Potentially guided bidirectionalized RRT* for fast optimal path planning in cluttered environments
Z.Tahir, A.H.Qureshi, Y.Ayaz, and R.Nawaz
International Journal of Robotics and Autonomous Systems, 2018. [paper] [arXiv]

Deeply Informed Neural Sampling For Robot Motion Planning
A.H.Qureshi and M.C.Yip
IEEE/RSJ International Conference on Intelligent Robot and Systems (IROS), 2018. [paper] [arXiv]

Adversarial Reward and Policy learning Via Variational Inverse Optimal Control
A.H.Qureshi, and M.C.Yip
Bay Area Machine Learning Symposium, 2018.

Re-planning Using Delaunay Triangulation for Real Time Motion Planning in Complex Dynamic Environments
A.H.Qureshi, Z.Tahir, G.Tariq, and Y.Ayaz
IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2018. [paper]

Deep reinforcement learning for human-robot interaction in the real-world
A.H.Qureshi
M.S. Thesis, Osaka University, 2017. [paper]

Show, Attend and Interact: Perceivable Social Human-Robot Interaction through Neural Attention Q-Network
A.H.Qureshi, Y.Nakamura, Y.Yoshikawa, and H.Ishiguro
IEEE/RAS International Conference on Robotics and Automation (ICRA), 2017. [paper] [arXiv]

Robot gains social intelligence through multimodal deep reinforcement learning
A.H.Qureshi, Y.Nakamura, Y.Yoshikawa, and H.Ishiguro
IEEE/RAS International Conference on Humanoid Robots, 2016. [paper] [arXiv]

Robot Learns Responsive Behavior through Interaction with People using Deep Reinforcement Learning
A.H.Qureshi, Y.Nakamura, Y.Yoshikawa, and H.Ishiguro
International Symposium on Cognitive Neuroscience Robotics, 2016.

Potential Functions Based Sampling Heuristic for Optimal Motion Planning
A.H.Qureshi and Y.Ayaz
Autonomous Robots, 2015. [paper] [arXiv]

Intelligent Bidirectional Rapidly-Exploring Random Trees for Optimal Motion Planning in Complex Cluttered Environments
A.H.Qureshi and Y.Ayaz
International Journal of Robotics and Autonomous Systems, 2015. [paper] [arXiv]

Triangular Geometrised Sampling Heuristic For RRT* Motion Planner
A.H.Qureshi, S.Mumtaz, Y.Ayaz, O.Hasan, M.S.Muhammad, and M.T.Mahmood
International Journal of Advanced Robotic Systems (IJARS), 2015. [paper]

Collaborative optimal reciprocal collision avoidance for mobile robots
S.A.Khan, Y.Ayaz, M.Jamil, S.O.Gillani, M.Naveed, A.H.Qureshi, and K.FIqbal
Journal of Control and Automation, 2015. [paper]

Augmenting RRT*-Planner with Local Trees for Motion Planning in Complex Dynamic Environments
A.H.Qureshi, S.Mumtaz, Y. Ayaz, and O. Hasan
IEEE/RAS International Conference on Methods and Models in Automation and Robotics (MMAR), 2014. [paper]

Enhanced RRT* for Motion Planning in Complex Cluttered Environments
A.H.Qureshi, and S.Mumtaz
B.S. Thesis, NUST, 2014.

Adaptive Potential Guided Directional RRT*
A.H.Qureshi, S.Mumtaz, Y.Ayaz, O.Hasan, and W.Y.Kim
IEEE/RAS International Conference on Robotics and Biomimetics (ROBIO), 2013. [paper]

Human tracking by a mobile robot using 3D features
B.Ali, A.H.Qureshi, Y.Ayaz, N.Muhammad, and W.Y.Kim
IEEE/RAS International Conference on Robotics and Biomimetics (ROBIO), 2013. [paper]

Potential guided directional-RRT* for accelerated motion planning in cluttered environments
A.H.Qureshi, K.F.Iqbal, S.M.Qamar, F.Islam, Y.Ayaz, and N.Muhammad
IEEE/RAS International Conference on Mechatronics and Automation (ICMA), 2013. [paper]

A solution to Perceptual Aliasing through Probabilistic Fuzzy Logic and SIFT
S.M.Qamar, K.F.Iqbal, A.H.Qureshi, N.Muhammad, Y.Ayaz, and A.G.Abbasi
IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2013. [paper]