10 papers
Identifying Driver Interactions via Conditional Behavior Prediction
Ekaterina Tolstaya, Reza Mahjourian, Carlton Downey +3
Interactive driving scenarios, such as lane changes, merges and unprotected turns, are some of the most challenging situations for autonomous driving. Planning in interactive scena…
Composable Learning with Sparse Kernel Representations
Ekaterina Tolstaya, Ethan Stump, Alec Koppel +1
We present a reinforcement learning algorithm for learning sparse non-parametric controllers in a Reproducing Kernel Hilbert Space. We improve the sample complexity of this approac…
Learning Connectivity for Data Distribution in Robot Teams
Ekaterina Tolstaya, Landon Butler, Daniel Mox +3
Many algorithms for control of multi-robot teams operate under the assumption that low-latency, global state information necessary to coordinate agent actions can readily be dissem…
Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning
Fernando Gama, Qingbiao Li, Ekaterina Tolstaya +2
Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information. While centralized controller…
Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks
Ekaterina Tolstaya, James Paulos, Vijay Kumar +1
The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection or search and rescue. We discretize the coverage problem to induce a…
Graph Neural Networks for Decentralized Controllers
Fernando Gama, Ekaterina Tolstaya, Alejandro Ribeiro
Dynamical systems comprised of autonomous agents arise in many relevant problems such as multi-agent robotics, smart grids, or smart cities. Controlling these systems is of paramou…