32 citations · 72 across the 24 of their papers we have counts for
24 papers
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…
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…
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…
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…
Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions
Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev
Heuristic search solvers like RTDP-Bel and LAO* have proven effective for computing optimal and bounded sub-optimal solutions for Partially Observable Markov Decision Processes (PO…
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…
ANAVI: Audio Noise Awareness using Visuals of Indoor environments for NAVIgation
Vidhi Jain, Rishi Veerapaneni, Yonatan Bisk
We propose Audio Noise Awareness using Visuals of Indoors for NAVIgation for quieter robot path planning. While humans are naturally aware of the noise they make and its impact on…
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,…
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…
A POMDP-based hierarchical planning framework for manipulation under pose uncertainty
Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev
Robots often face challenges in domestic environments where visual feedback is ineffective, such as retrieving objects obstructed by occlusions or finding a light switch in the dar…
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…
A preprocessing-based planning framework for utilizing contacts in high-precision insertion tasks
Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev
In manipulation tasks like plug insertion or assembly that have low tolerance to errors in pose estimation (errors of the order of 2mm can cause task failure), the utilization of t…
A Data Efficient Framework for Learning Local Heuristics
Rishi Veerapaneni, Jonathan Park, Muhammad Suhail Saleem +1
With the advent of machine learning, there have been several recent attempts to learn effective and generalizable heuristics. Local Heuristic A* (LoHA*) is one recent method that i…
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…
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities
He Jiang, Yulun Zhang, Rishi Veerapaneni +1
Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new g…
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…
Improving Learnt Local MAPF Policies with Heuristic Search
Rishi Veerapaneni, Qian Wang, Kevin Ren +3
Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations. State-of-the-art classical MAPF solvers typically…
Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution
Yifan Su, Rishi Veerapaneni, Jiaoyang Li
The Multi-Agent Path Finding (MAPF) problem involves planning collision-free paths for multiple agents in a shared environment. The majority of MAPF solvers rely on the assumption…
Learning Local Heuristics for Search-Based Navigation Planning
Rishi Veerapaneni, Muhammad Suhail Saleem, Maxim Likhachev
Graph search planning algorithms for navigation typically rely heavily on heuristics to efficiently plan paths. As a result, while such approaches require no training phase and can…
Non-Blocking Batch A* (Technical Report)
Rishi Veerapaneni, Maxim Likhachev
Heuristic search has traditionally relied on hand-crafted or programmatically derived heuristics. Neural networks (NNs) are newer powerful tools which can be used to learn complex…
Effective Integration of Weighted Cost-to-go and Conflict Heuristic within Suboptimal CBS
Rishi Veerapaneni, Tushar Kusnur, Maxim Likhachev
Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts.…
Entity Abstraction in Visual Model-Based Reinforcement Learning
Rishi Veerapaneni, John D. Co-Reyes, Michael Chang +5
This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit i…