86 citations · 251 across the 40 of their papers we have counts for
6 papers · 1 filter
Constant-time Motion Planning with Anytime Refinement for Manipulation
Itamar Mishani, Hayden Feddock, Maxim Likhachev
Robotic manipulators are essential for future autonomous systems, yet limited trust in their autonomy has confined them to rigid, task-specific systems. The intricate configuration…
A-ePA*SE: Anytime Edge-Based Parallel A* for Slow Evaluations
Hanlan Yang, Shohin Mukherjee, Maxim Likhachev
Anytime search algorithms are useful for planning problems where a solution is desired under a limited time budget. Anytime algorithms first aim to provide a feasible solution quic…
Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How
Dhruv Saxena, Maxim Likhachev
We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide,…
Planning for Complex Non-prehensile Manipulation Among Movable Objects by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation
Dhruv Mauria Saxena, Maxim Likhachev
Real-world manipulation problems in heavy clutter require robots to reason about potential contacts with objects in the environment. We focus on pick-and-place style tasks to retri…
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…
GePA*SE: Generalized Edge-Based Parallel A* for Slow Evaluations
Shohin Mukherjee, Maxim Likhachev
Parallel search algorithms have been shown to improve planning speed by harnessing the multithreading capability of modern processors. One such algorithm PA*SE achieves this by par…