3 citations · 9 across the 27 of their papers we have counts for
11 papers · 1 filter
Implicit Graph Search for Planning on Graphs of Convex Sets
Ramkumar Natarajan, Chaoqi Liu, Howie Choset +1
Graphs of Convex Sets (GCS) is a recent method for synthesizing smooth trajectories by decomposing the planning space into convex sets, forming a graph to encode the adjacency rela…
RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
Shivam Vats, Devesh K. Jha, Maxim Likhachev +2
Model-based planners and controllers are commonly used to solve complex manipulation problems as they can efficiently optimize diverse objectives and generalize to long horizon tas…
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…
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 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…
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…
Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
Yorai Shaoul, Itamar Mishani, Maxim Likhachev +1
An exciting frontier in robotic manipulation is the use of multiple arms at once. However, planning concurrent motions is a challenging task using current methods. The high-dimensi…
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…