7 citations · 33 across the 47 of their papers we have counts for
53 papers
Shape-Aware Neural Time Fields for Rigid-Body Motion and Manipulation Planning
Yanming Zhou, Yuchen Liu, Ahmed H. Qureshi
Rigid-body motion planning is fundamental to robotic transport and manipulation, where both object position and orientation determine whether a body can move through cluttered envi…
Riemannian Splat Regression Models for Learning Time Fields on Arbitrary Riemannian Manifolds
Anastasios Manganaris, Rohit Banerjee, Gokul Prabhakaran +1
Motion planning on arbitrary Riemannian manifolds is an important and difficult problem that frustrates typical planning methods for Euclidean spaces. In particular, motion plannin…
Missing Bridges: Composition-Aware Active Imitation Learning
Maxwell J. Jacobson, Ahmed H Qureshi, Yexiang Xue
Active imitation learning reduces expert effort by allowing a learner to request the demonstrations it needs. Existing methods typically select these requests for their expected in…
Coordinated Motion Planning for Multi-Arm Systems via Iterative LQ Games
Junyoung Kim, Hanwen Ren, Lei Zhang +1
Multi-agent motion planning for high-degree-of-freedom robotics manipulators in shared workspaces remains a fundamental yet challenging problem. Centralized planners often suffer f…
Composing Learned Robot Behaviors with Temporal Logic at Runtime
Moritz Zoellner, Anastasios Manganaris, Ahmed H. Qureshi +1
Executing Linear Temporal Logic (LTL) instructions with learned robot policies faces two practical challenges: demonstrations may cover individual behaviors without containing the…
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
Qingyi Chen, Zachary Kingston, Ahmed H. Qureshi
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized plan…
Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics
Vittorio Giammarino, Anastasios Manganaris, Ahmed H. Qureshi
Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs. Goal-conditioned reinforcement learning (GCR…
Weakly-supervised Learning for Physics-informed Neural Motion Planning via Sparse Roadmap
Ruiqi Ni, Yuchen Liu, Ahmed H. Qureshi
The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer…
Graph-of-Constraints Model Predictive Control for Reactive Multi-agent Task and Motion Planning
Anastasios Manganaris, Jeremy Lu, Ahmed H. Qureshi +1
Sequences of interdependent geometric constraints are central to many multi-agent Task and Motion Planning (TAMP) problems. However, existing methods for handling such constraint s…
PPGuide: Steering Diffusion Policies with Performance Predictive Guidance
Zixing Wang, Devesh K. Jha, Ahmed H. Qureshi +1
Diffusion policies have shown to be very efficient at learning complex, multi-modal behaviors for robotic manipulation. However, errors in generated action sequences can compound o…
Safe Continuous-time Multi-Agent Reinforcement Learning via Epigraph Form
Xuefeng Wang, Lei Zhang, Henglin Pu +2
Multi-agent reinforcement learning (MARL) has made significant progress in recent years, but most algorithms still rely on a discrete-time Markov Decision Process (MDP) with fixed…
Multi-Agent Monte Carlo Tree Search for Makespan-Efficient Object Rearrangement in Cluttered Spaces
Hanwen Ren, Junyong Kim, Aathman Tharmasanthiran +1
Object rearrangement planning in complex, cluttered environments is a common challenge in warehouses, households, and rescue sites. Prior studies largely address monotone instances…
Formal Methods in Robot Policy Learning and Verification: A Survey on Current Techniques and Future Directions
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi +1
As hardware and software systems have grown in complexity, formal methods have been indispensable tools for rigorously specifying acceptable behaviors, synthesizing programs to mee…
Goal Reaching with Eikonal-Constrained Hierarchical Quasimetric Reinforcement Learning
Vittorio Giammarino, Ahmed H. Qureshi
Goal-Conditioned Reinforcement Learning (GCRL) mitigates the difficulty of reward design by framing tasks as goal reaching rather than maximizing hand-crafted reward signals. In th…
Automaton Constrained Q-Learning
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi
Real-world robotic tasks often require agents to achieve sequences of goals while respecting time-varying safety constraints. However, standard Reinforcement Learning (RL) paradigm…
Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning
Qingyi Chen, Ruiqi Ni, Junyoung Kim +1
Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots to navigate dynamic environment…
Robust Point Cloud Reinforcement Learning via PCA-Based Canonicalization
Michael Bezick, Vittorio Giammarino, Ahmed H. Qureshi
Reinforcement Learning (RL) from raw visual input has achieved impressive successes in recent years, yet it remains fragile to out-of-distribution variations such as changes in lig…
Physics-informed Value Learner for Offline Goal-Conditioned Reinforcement Learning
Vittorio Giammarino, Ruiqi Ni, Ahmed H. Qureshi
Offline Goal-Conditioned Reinforcement Learning (GCRL) holds great promise for domains such as autonomous navigation and locomotion, where collecting interactive data is costly and…
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…
Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning
Xuefeng Wang, Lei Zhang, Henglin Pu +2
Existing reinforcement learning (RL) methods struggle with complex dynamical systems that demand interactions at high frequencies or irregular time intervals. Continuous-time RL (C…
Multimodal Human-Intent Modeling for Contextual Robot-to-Human Handovers of Arbitrary Objects
Lucas Chen, Guna Avula, Hanwen Ren +2
Human-robot object handover is a crucial element for assistive robots that aim to help people in their daily lives, including elderly care, hospitals, and factory floors. The exist…
Physics-informed Neural Time Fields for Prehensile Object Manipulation
Hanwen Ren, Ruiqi Ni, Ahmed H. Qureshi
Object manipulation skills are necessary for robots operating in various daily-life scenarios, ranging from warehouses to hospitals. They allow the robots to manipulate the given o…
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…
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
Qingyi Chen, Zachary Kingston, Ahmed H. Qureshi
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized plan…