7 citations · 33 across the 47 of their papers we have counts for
13 papers · 1 filter
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…