32 citations · 72 across the 24 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…