Ahmed H. Qureshi

research

∙ 09/09/2023

DeRi-IGP: Manipulating Rigid Objects Using Deformable Objects via Iterative Grasp-Pull

Heterogeneous systems manipulation, i.e., manipulating rigid objects via...

0 Zixing Wang, et al. ∙

research

∙ 09/09/2023

AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations

Anytime 3D human pose forecasting is crucial to synchronous real-world h...

0 Zixing Wang, et al. ∙

research

∙ 09/05/2023

Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning

Robotic manipulation tasks, such as object rearrangement, play a crucial...

0 Manav Kulshrestha, et al. ∙

research

∙ 07/30/2023

Efficient Q-Learning over Visit Frequency Maps for Multi-agent Exploration of Unknown Environments

The robot exploration task has been widely studied with applications spa...

0 Xuyang Chen, et al. ∙

research

∙ 06/26/2023

SIMF: Semantics-aware Interactive Motion Forecasting for Autonomous Driving

Autonomous vehicles require motion forecasting of their surrounding mult...

0 Vidyaa Krishnan Nivash, et al. ∙

research

∙ 06/01/2023

Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformers

Motion planning is integral to robotics applications such as autonomous ...

0 Jacob J. Johnson, et al. ∙

research

∙ 06/01/2023

Progressive Learning for Physics-informed Neural Motion Planning

Motion planning (MP) is one of the core robotics problems requiring fast...

0 Ruiqi Ni, et al. ∙

research

∙ 05/26/2023

Multi-Stage Monte Carlo Tree Search for Non-Monotone Object Rearrangement Planning in Narrow Confined Environments

Non-monotone object rearrangement planning in confined spaces such as ca...

0 Hanwen Ren, et al. ∙

research

∙ 05/22/2023

DeRi-Bot: Learning to Collaboratively Manipulate Rigid Objects via Deformable Objects

Recent research efforts have yielded significant advancements in manipul...

0 Zixing Wang, et al. ∙

research

∙ 03/14/2023

Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies

Recent work has shown the promise of creating generalist, transformer-ba...

0 Daniel Lawson, et al. ∙

research

∙ 03/02/2023

Co-learning Planning and Control Policies Using Differentiable Formal Task Constraints

This paper presents a hierarchical reinforcement learning algorithm cons...

0 Zikang Xiong, et al. ∙

research

∙ 11/11/2022

Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling

Learning long-horizon tasks such as navigation has presented difficult c...

0 Daniel Lawson, et al. ∙

research

∙ 10/06/2022

CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration

Robot grasping is an actively studied area in robotics, mainly focusing ...

0 Abhinav K. Keshari, et al. ∙

research

∙ 09/30/2022

NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning

Neural Motion Planners (NMPs) have emerged as a promising tool for solvi...

0 Ruiqi Ni, et al. ∙

research

∙ 08/23/2022

Robot Active Neural Sensing and Planning in Unknown Cluttered Environments

Active sensing and planning in unknown, cluttered environments is an ope...

0 Hanwen Ren, et al. ∙

research

∙ 05/21/2022

Co-design of Embodied Neural Intelligence via Constrained Evolution

We introduce a novel co-design method for autonomous moving agents' shap...

8 Zhiquan Wang, et al. ∙

research

∙ 03/02/2022

Model-free Neural Lyapunov Control for Safe Robot Navigation

Model-free Deep Reinforcement Learning (DRL) controllers have demonstrat...

12 Zikang Xiong, et al. ∙

research

∙ 06/05/2021

Motion Planning Transformers: One Model to Plan Them All

Transformers have become the powerhouse of natural language processing a...

0 Jacob J. Johnson, et al. ∙

research

∙ 06/02/2021

NeRP: Neural Rearrangement Planning for Unknown Objects

Robots will be expected to manipulate a wide variety of objects in compl...

42 Ahmed H. Qureshi, et al. ∙

research

∙ 01/17/2021

MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints

Kinodynamic Motion Planning (KMP) is to find a robot motion subject to c...

0 Linjun Li, et al. ∙

research

∙ 10/17/2020

Constrained Motion Planning Networks X

Constrained motion planning is a challenging field of research, aiming f...

0 Ahmed H. Qureshi, et al. ∙

research

∙ 08/12/2020

Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots

Reliable real-time planning for robots is essential in today's rapidly e...

0 Jacob J. Johnson, et al. ∙

research

∙ 08/09/2020

Neural Manipulation Planning on Constraint Manifolds

The presence of task constraints imposes a significant challenge to moti...

0 Ahmed H. Qureshi, et al. ∙

research

∙ 07/13/2019

Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners

This paper describes Motion Planning Networks (MPNet), a computationally...

3 Ahmed H. Qureshi, et al. ∙

research

∙ 05/25/2019

Composing Ensembles of Policies with Deep Reinforcement Learning

Composition of elementary skills into complex behaviors to solve challen...

0 Ahmed H. Qureshi, et al. ∙

research

∙ 04/25/2019

Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation

Fast and efficient path generation is critical for robots operating in c...

32 Mayur J. Bency, et al. ∙

research

∙ 09/26/2018

Deeply Informed Neural Sampling for Robot Motion Planning

Sampling-based Motion Planners (SMPs) have become increasingly popular a...

0 Ahmed H. Qureshi, et al. ∙

research

∙ 09/17/2018

Adversarial Imitation via Variational Inverse Reinforcement Learning

We consider a problem of learning a reward and policy from expert exampl...

2 Ahmed H. Qureshi, et al. ∙

research

∙ 07/22/2018

Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments

Rapidly-exploring Random Tree star (RRT*) has recently gained immense po...

0 Zaid Tahir, et al. ∙

research

∙ 06/14/2018

Motion Planning Networks

Fast and efficient motion planning algorithms are crucial for many state...

0 Ahmed H. Qureshi, et al. ∙

Ahmed H. Qureshi

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