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Ekaterina Tolstaya

7 accepted papers

2021

Identifying Driver Interactions via Conditional Behavior Prediction

Ekaterina Tolstaya, Reza Mahjourian, Carlton Downey, Balakrishnan Vadarajan, Benjamin Sapp, Dragomir Anguelov
ICRA 2021poster

Interactive driving scenarios, such as lane changes, merges and unprotected turns, are some of the most challenging situations for autonomous driving. Planning in interactive scenarios requires accurately modeling the reactions of other agents to different future actions of the ego agent. We develop…

Cited by 89Source ↗Scholar ↗
2021

Learning Connectivity for Data Distribution in Robot Teams

Ekaterina Tolstaya, Landon Butler, Daniel Mox, James Paulos, Vijay Kumar, Alejandro Ribeiro
IROS 2021poster

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 disseminated among the team. However, in harsh environments with no existing communication infrastructure, robots must form ad…

Cited by 12Source ↗code ↗Scholar ↗
2021

Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks

Ekaterina Tolstaya, James Paulos, Vijay Kumar, Alejandro Ribeiro
IROS 2021poster

The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection, exploration, or search and rescue. We discretize the coverage problem to induce a spatial graph of locations and represent robots as nodes in the graph. Then, we train a Graph Neural Netwo…

Cited by 79Source ↗Scholar ↗
2019

Graph Policy Gradients for Large Scale Robot Control

Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar
CoRL 2019

In this paper, the problem of learning policies to control a large number of homogeneous robots is considered. To this end, we propose a new algorithm we call Graph Policy Gradients (GPG) that exploits the underlying graph symmetry among the robots. The curse of dimensionality one encounters when wo

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2019

Inverse Optimal Planning for Air Traffic Control

Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar, Ashish Kapoor
IROS 2019poster

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 develop a method to learn the rules of air traffic control from real data as a cost function via maximum entropy inverse re…

Cited by 8Source ↗code ↗Scholar ↗
2019

Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

Ekaterina Tolstaya, Fernando Gama, James Paulos, George Pappas, Vijay Kumar, Alejandro Ribeiro
CoRL 2019

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 learn local controllers that require only local information and communications at test time by imitating the policy of ce

Cited by 0Source ↗PDF ↗code ↗Scholar ↗