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Found 10 papers, 3 papers with code
FireFly A Synthetic Dataset for Ember Detection in Wildfire
This paper presents "FireFly", a synthetic dataset for ember detection created using Unreal Engine 4 (UE4), designed to overcome the current lack of ember-specific training resources.
MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings
Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization.
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments
In this paper, we propose a meta-reinforcement learning approach to learn the dynamic model of a non-stationary environment to be used for meta-policy optimization later.
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation.
Robotics
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities.
Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots
We propose an adaptive solution for the problem where stochastic real-world dynamics are modeled as a Gaussian Process (GP).
Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems
In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account.
Path Planning with Kinematic Constraints for Robot Groups
Path planning for multiple robots is well studied in the AI and robotics communities.
Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research.
Multiplayer Games for Learning Multirobot Coordination Algorithms
This ability, if learned as a set of distributed multirobot coordination strategies, can enable programming large groups of robots to collaborate towards complex coordination objectives in a way similar to humans.
