41 citations · 188 across the 52 of their papers we have counts for
10 papers · 1 filter
Towards True Lossless Sparse Communication in Multi-Agent Systems
Seth Karten, Mycal Tucker, Siva Kailas +1
Communication enables agents to cooperate to achieve their goals. Learning when to communicate, i.e., sparse (in time) communication, and whom to message is particularly important…
Explainable Action Advising for Multi-Agent Reinforcement Learning
Yue Guo, Joseph Campbell, Simon Stepputtis +4
Action advising is a knowledge transfer technique for reinforcement learning based on the teacher-student paradigm. An expert teacher provides advice to a student during training i…
Control Barrier Functions-based Semi-Definite Programs (CBF-SDPs): Robust Safe Control For Dynamic Systems with Relative Degree Two Safety Indices
Jaskaran Singh Grover, Changliu Liu, Katia Sycara
In this draft article, we consider the problem of achieving safe control of a dynamic system for which the safety index or (control barrier function (loosely)) has relative degree…
The Before, During, and After of Multi-Robot Deadlock
Jaskaran Grover, Changliu Liu, Katia Sycara
Collision avoidance for multirobot systems is a well-studied problem. Recently, control barrier functions (CBFs) have been proposed for synthesizing controllers that guarantee coll…
ARC - Actor Residual Critic for Adversarial Imitation Learning
Ankur Deka, Changliu Liu, Katia Sycara
Adversarial Imitation Learning (AIL) is a class of popular state-of-the-art Imitation Learning algorithms commonly used in robotics. In AIL, an artificial adversary's misclassifica…
Noncooperative Herding With Control Barrier Functions: Theory and Experiments
Jaskaran Grover, Nishant Mohanty, Wenhao Luo +2
In this paper, we consider the problem of protecting a high-value unit from inadvertent attack by a group of agents using defending robots. Specifically, we develop a control strat…
Configuration Control for Physical Coupling of Heterogeneous Robot Swarms
Sha Yi, Zeynep Temel, Katia Sycara
In this paper, we present a heterogeneous robot swarm system that can physically couple with each other to form functional structures and dynamically decouple to perform individual…
PuzzleBots: Physical Coupling of Robot Swarms
Sha Yi, Zeynep Temel, Katia Sycara
Robot swarms have been shown to improve the ability of individual robots by inter-robot collaboration. In this paper, we present the PuzzleBots - a low-cost robotic swarm system wh…
Probe-Based Interventions for Modifying Agent Behavior
Mycal Tucker, William Kuhl, Khizer Shahid +3
Neural nets are powerful function approximators, but the behavior of a given neural net, once trained, cannot be easily modified. We wish, however, for people to be able to influen…
Interpretable Learned Emergent Communication for Human-Agent Teams
Seth Karten, Mycal Tucker, Huao Li +3
Learning interpretable communication is essential for multi-agent and human-agent teams (HATs). In multi-agent reinforcement learning for partially-observable environments, agents…