1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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…
On the Effectiveness of Iterative Learning Control
Anirudh Vemula, Wen Sun, Maxim Likhachev +1
Iterative learning control (ILC) is a powerful technique for high performance tracking in the presence of modeling errors for optimal control applications. There is extensive prior…