41 citations · 188 across the 52 of their papers we have counts for
56 papers
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning
Ini Oguntola, Joseph Campbell, Simon Stepputtis +1
The ability to model the mental states of others is crucial to human social intelligence, and can offer similar benefits to artificial agents with respect to the social dynamics in…
Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree
Yimin Tang, Zhongqiang Ren, Jiaoyang Li +1
Combined Target-Assignment and Path-Finding problem (TAPF) requires simultaneously assigning targets to agents and planning collision-free paths for agents from their start locatio…
Reconfigurable Robot Control Using Flexible Coupling Mechanisms
Sha Yi, Katia Sycara, Zeynep Temel
Reconfigurable robot swarms are capable of connecting with each other to form complex structures. Current mechanical or magnetic connection mechanisms can be complicated to manufac…
Introspective Action Advising for Interpretable Transfer Learning
Joseph Campbell, Yue Guo, Fiona Xie +2
Transfer learning can be applied in deep reinforcement learning to accelerate the training of a policy in a target task by transferring knowledge from a policy learned in a related…
Sample-Efficient Learning of Novel Visual Concepts
Sarthak Bhagat, Simon Stepputtis, Joseph Campbell +1
Despite the advances made in visual object recognition, state-of-the-art deep learning models struggle to effectively recognize novel objects in a few-shot setting where only a lim…
Characterizing Out-of-Distribution Error via Optimal Transport
Yuzhe Lu, Yilong Qin, Runtian Zhai +7
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important…
Graph-based Simultaneous Coverage and Exploration Planning for Fast Multi-robot Search
Indraneel Patil, Rachel Zheng, Charvi Gupta +3
In large unknown environments, search operations can be much more time-efficient with the use of multi-robot fleets by parallelizing efforts. This means robots must efficiently per…
On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning
Seth Karten, Siva Kailas, Huao Li +1
Explicit communication among humans is key to coordinating and learning. Social learning, which uses cues from experts, can greatly benefit from the usage of explicit communication…
Concept Learning for Interpretable Multi-Agent Reinforcement Learning
Renos Zabounidis, Joseph Campbell, Simon Stepputtis +2
Multi-agent robotic systems are increasingly operating in real-world environments in close proximity to humans, yet are largely controlled by policy models with inscrutable deep ne…
Predicting Out-of-Distribution Error with Confidence Optimal Transport
Yuzhe Lu, Zhenlin Wang, Runtian Zhai +3
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops. Being able to estimate…
Distributed Multirobot Control for Non-Cooperative Herding
Nishant Mohanty, Jaskaran Grover, Changliu Liu +1
In this paper, we consider the problem of protecting a high-value area from being breached by sheep agents by crafting motions for dog robots. We use control barrier functions to p…
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…
SS-MAIL: Self-Supervised Multi-Agent Imitation Learning
Akshay Dharmavaram, Tejus Gupta, Jiachen Li +1
The current landscape of multi-agent expert imitation is broadly dominated by two families of algorithms - Behavioral Cloning (BC) and Adversarial Imitation Learning (AIL). BC appr…
Emergent Discrete Communication in Semantic Spaces
Mycal Tucker, Huao Li, Siddharth Agrawal +4
Neural agents trained in reinforcement learning settings can learn to communicate among themselves via discrete tokens, accomplishing as a team what agents would be unable to do al…
Learning Embeddings that Capture Spatial Semantics for Indoor Navigation
Vidhi Jain, Prakhar Agarwal, Shishir Patil +1
Incorporating domain-specific priors in search and navigation tasks has shown promising results in improving generalization and sample complexity over end-to-end trained policies.…