4 citations · 7 across the 5 of their papers we have counts for
5 papers
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…
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…
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…