73 citations · 115 across the 28 of their papers we have counts for
28 papers
LieCraft: A Multi-Agent Framework for Evaluating Deceptive Capabilities in Language Models
Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck +5
Large Language Models (LLMs) exhibit impressive general-purpose capabilities but also introduce serious safety risks, particularly the potential for deception as models acquire inc…
Model-Agnostic Policy Explanations with Large Language Models
Zhang Xi-Jia, Yue Guo, Shufei Chen +4
Intelligent agents, such as robots, are increasingly deployed in real-world, human-centric environments. To foster appropriate human trust and meet legal and ethical standards, the…
Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning
Muhan Lin, Shuyang Shi, Yue Guo +7
Credit assignment, the process of attributing credit or blame to individual agents for their contributions to a team's success or failure, remains a fundamental challenge in multi-…
Symbolic Graph Inference for Compound Scene Understanding
FNU Aryan, Simon Stepputtis, Sarthak Bhagat +4
Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn…
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models
Muhan Lin, Shuyang Shi, Yue Guo +6
The correct specification of reward models is a well-known challenge in reinforcement learning. Hand-crafted reward functions often lead to inefficient or suboptimal policies and m…
Multi-Agent Transfer Learning via Temporal Contrastive Learning
Weihao Zeng, Joseph Campbell, Simon Stepputtis +1
This paper introduces a novel transfer learning framework for deep multi-agent reinforcement learning. The approach automatically combines goal-conditioned policies with temporal c…
ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition
Samuel Li, Sarthak Bhagat, Joseph Campbell +4
Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments. Inspired by the human capability to grasp such objects through intuiti…
HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation
Ce Zhang, Simon Stepputtis, Joseph Campbell +2
Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabli…
Benchmarking and Enhancing Disentanglement in Concept-Residual Models
Renos Zabounidis, Ini Oguntola, Konghao Zhao +3
Concept bottleneck models (CBMs) are interpretable models that first predict a set of semantically meaningful features, i.e., concepts, from observations that are subsequently used…
Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation
Xijia Zhang, Yue Guo, Simon Stepputtis +2
Intelligent agents such as robots are increasingly deployed in real-world, safety-critical settings. It is vital that these agents are able to explain the reasoning behind their de…
Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
Simon Stepputtis, Joseph Campbell, Yaqi Xie +6
Deception and persuasion play a critical role in long-horizon dialogues between multiple parties, especially when the interests, goals, and motivations of the participants are not…
Theory of Mind for Multi-Agent Collaboration via Large Language Models
Huao Li, Yu Quan Chong, Simon Stepputtis +4
While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexpl…
Explaining Agent Behavior with Large Language Models
Xijia Zhang, Yue Guo, Simon Stepputtis +2
Intelligent agents such as robots are increasingly deployed in real-world, safety-critical settings. It is vital that these agents are able to explain the reasoning behind their de…
Knowledge-Guided Short-Context Action Anticipation in Human-Centric Videos
Sarthak Bhagat, Simon Stepputtis, Joseph Campbell +1
This work focuses on anticipating long-term human actions, particularly using short video segments, which can speed up editing workflows through improved suggestions while fosterin…
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…
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…
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…
Learning and Blending Robot Hugging Behaviors in Time and Space
Michael Drolet, Joseph Campbell, Heni Ben Amor
We introduce an imitation learning-based physical human-robot interaction algorithm capable of predicting appropriate robot responses in complex interactions involving a superposit…
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…
Learning Predictive Models for Ergonomic Control of Prosthetic Devices
Geoffrey Clark, Joseph Campbell, Heni Ben Amor
We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanic…
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Simon Stepputtis, Joseph Campbell, Mariano Phielipp +3
Imitation learning is a popular approach for teaching motor skills to robots. However, most approaches focus on extracting policy parameters from execution traces alone (i.e., moti…