3 citations · 7 across the 23 of their papers we have counts for
26 papers
A Theoretical Framework for Parallel Lifelong MAPF Using Group Decentralized Planning
Alex DeWeese, Jiaoyang Li, Guannan Qu
In the Lifelong Multi-Agent Path Finding (L-MAPF) problem, agents must repeatedly move from one destination to another while avoiding obstacles and inter-agent collisions. Widely r…
Scalable Long-Horizon Planning with Staggered Updates for Lifelong MAPF
Vaibhav Sanjay, Jiaoyang Li
Lifelong Multi-Agent Path Finding (LMAPF) requires generating collision-free paths for large agent fleets under strict real-time constraints. Reactive frameworks such as PIBT and E…
Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations
He Jiang, Jingtian Yan, Yulun Zhang +5
Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones. While m…
Embodying Multi-Hand Manipulation Policies by Searching the Assignment and Null Spaces
Yorai Shaoul, Jiaoyang Li, Maxim Likhachev
Learned manipulation policies increasingly predict motions for abstract "hands" and are attractive in practice because they rely on easily collected demonstrations and transfer acr…
Model-Based Diffusion Optimal Control for Multi-Robot Motion Planning
Zhilin He, Yorai Shaoul, Jiaoyang Li
Multi-Robot Motion Planning in continuous environments, where robots must generate dynamically feasible, collision-free trajectories, is challenging due to the combinatorial growth…
Diffusion for Long-Horizon Multi-Robot Path Planning in Human-Shared Environments
Vaibhav Sanjay, Yorai Shaoul, Jiaoyang Li
Multi-robot path planning in human-shared environments requires a delicate balance between robust inter-robot coordination and socially aware behavior. While diffusion models excel…
LAMP: Long-Horizon Adaptive Manipulation Planning for Multi-Robot Collaboration in Cluttered Space
Shuai Zhou, Yorai Shaoul, Jiaoyang Li
Multi-robot manipulation requires jointly reasoning about contact formations, robot motions under coupled dynamics, and collision avoidance. Systematically searching over this larg…
Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding
Yulun Zhang, Varun Bhatt, Matthew C. Fontaine +2
Multi-Agent Path Finding (MAPF) aims to move agents from their start to goal vertices on a graph. Lifelong MAPF (LMAPF) continuously assigns new goals to agents as they complete cu…
Planning over MAPF Agent Dependencies via Multi-Dependency PIBT
Zixiang Jiang, Yulun Zhang, Rishi Veerapaneni +1
Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms. Pr…
Analyzing Planner Design Trade-offs for MAPF under ADG-based Realistic Execution
Jingtian Yan, Zhifei Li, William Kang +2
Multi-Agent Path Finding (MAPF) algorithms are increasingly deployed in industrial warehouses and automated manufacturing facilities, where robots must operate reliably under real-…
Collaborative Multi-Robot Non-Prehensile Manipulation via Flow-Matching Co-Generation
Yorai Shaoul, Zhe Chen, Mohamed Naveed Gul Mohamed +3
Coordinating a team of robots to reposition multiple objects in cluttered environments requires reasoning jointly about where robots should establish contact, how to manipulate obj…
Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks
Rishi Veerapaneni, Alvin Tang, Haodong He +9
Imagine the future construction site, hospital, or office with dozens of robots bought from different manufacturers. How can we enable these different robots to effectively move in…
SRMP: Search-Based Robot Motion Planning Library
Itamar Mishani, Yorai Shaoul, Ramkumar Natarajan +2
Motion planning is a critical component in any robotic system. Over the years, powerful tools like the Open Motion Planning Library (OMPL) have been developed, offering numerous mo…
Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees
Tiannan Zhang, Rishi Veerapaneni, Shao-Hung Chan +2
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for a team of agents. Although several MAPF methods which solve full-horizon MAPF have compl…
BTPG-max: Achieving Local Maximal Bidirectional Pairs for Bidirectional Temporal Plan Graphs
Yifan Su, Rishi Veerapaneni, Jiaoyang Li
Multi-Agent Path Finding (MAPF) requires computing collision-free paths for multiple agents in shared environment. Most MAPF planners assume that each agent reaches a specific loca…
Anytime Single-Step MAPF Planning with Anytime PIBT
Nayesha Gandotra, Rishi Veerapaneni, Muhammad Suhail Saleem +3
PIBT is a popular Multi-Agent Path Finding (MAPF) method at the core of many state-of-the-art MAPF methods including LaCAM, CS-PIBT, and WPPL. The main utility of PIBT is that it i…
Real-Time LaCAM for Real-Time MAPF
Runzhe Liang, Rishi Veerapaneni, Daniel Harabor +2
The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too lon…
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding
He Jiang, Yutong Wang, Rishi Veerapaneni +3
Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently,…
Multi-Robot Motion Planning with Diffusion Models
Yorai Shaoul, Itamar Mishani, Shivam Vats +2
Diffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mo…
Windowed MAPF with Completeness Guarantees
Rishi Veerapaneni, Muhammad Suhail Saleem, Jiaoyang Li +1
Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF sy…
Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF
Rishi Veerapaneni, Arthur Jakobsson, Kevin Ren +3
Multi-Agent Path Finding (MAPF) is the problem of effectively finding efficient collision-free paths for a group of agents in a shared workspace. The MAPF community has largely foc…
LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding
Yutong Wang, Tanishq Duhan, Jiaoyang Li +1
Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known enviro…
Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality
Yorai Shaoul, Rishi Veerapaneni, Maxim Likhachev +1
Multi-Robot-Arm Motion Planning (M-RAMP) is a challenging problem featuring complex single-agent planning and multi-agent coordination. Recent advancements in extending the popular…
From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS
Yu Wu, Rishi Veerapaneni, Jiaoyang Li +1
The majority of multi-agent path finding (MAPF) methods compute collision-free space-time paths which require agents to be at a specific location at a specific discretized timestep…