410 citations · 441 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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 Policy Gradients for Large Scale Robot Control
Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro +1
In this paper, we consider the problem of learning policies to control a large number of homogeneous robots. To this end, we propose a new algorithm we call Graph Policy Gradients…
Inverse Optimal Planning for Air Traffic Control
Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar +1
We envision a system that concisely describes the rules of air traffic control, assists human operators and supports dense autonomous air traffic around commercial airports. We dev…
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks
Ekaterina Tolstaya, Fernando Gama, James Paulos +3
We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to l…