Search Results for author:
Found 11 papers, 3 papers with code
Terraforming -- Environment Manipulation during Disruptions for Multi-Agent Pickup and Delivery
In automated warehouses, teams of mobile robots fulfill the packaging process by transferring inventory pods to designated workstations while navigating narrow aisles formed by tightly packed pods.
Wall Street Tree Search: Risk-Aware Planning for Offline Reinforcement Learning
However, this promise also bears the drawback of this setting as the restricted dataset induces uncertainty because the agent can encounter unfamiliar sequences of states and actions that the training data did not cover.
T*$\varepsilon$ -- Bounded-Suboptimal Efficient Motion Planning for Minimum-Time Planar Curvature-Constrained Systems
We demonstrate using empirical evaluation that our planning framework can reduce the runtime by several orders of magnitude compared to the state-of-the-art while still providing guarantees on the quality of the solution.
Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via Environment Manipulation
Multi-agent pathfinding (MAPF) is concerned with planning collision-free paths for a team of agents from their start to goal locations in an environment cluttered with obstacles.
Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation
A central challenge in building robotic prostheses is the creation of a sensor-based system able to read physiological signals from the lower limb and instruct a robotic hand to perform various tasks.
Leveraging Experience in Lifelong Multi-Agent Pathfinding
Therefore, a solution to one query informs the next query, which leads to similarity with respect to the agents' start and goal positions, and how collisions need to be resolved from one query to the next.
Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance
We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated.
Revisiting the Complexity Analysis of Conflict-Based Search: New Computational Techniques and Improved Bounds
In this work we revisit the complexity analysis of CBS to provide tighter bounds on the algorithm's run-time in the worst-case.
A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction
We evaluate our framework in simulation as well as the real world.
Anytime Motion Planning on Large Dense Roadmaps with Expensive Edge Evaluations
We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are computationally expensive.
Robotics
The Provable Virtue of Laziness in Motion Planning
The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target.
Robotics Data Structures and Algorithms
