41 citations · 188 across the 52 of their papers we have counts for
11 papers · 1 filter
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…