Search Results for author:
Found 25 papers, 5 papers with code
ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition
Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments.
HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation
Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches.
Benchmarking and Enhancing Disentanglement in Concept-Residual Models
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 to condition a downstream task.
Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation
Intelligent agents such as robots are increasingly deployed in real-world, safety-critical settings.
Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
We discuss the capabilities of LLMs to utilize deceptive long-horizon conversations between six human players to determine each player's goal and motivation.
Theory of Mind for Multi-Agent Collaboration via Large Language Models
While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored.
Explaining Agent Behavior with Large Language Models
Intelligent agents such as robots are increasingly deployed in real-world, safety-critical settings.
Knowledge-Guided Short-Context Action Anticipation in Human-Centric Videos
This work focuses on anticipating long-term human actions, particularly using short video segments, which can speed up editing workflows through improved suggestions while fostering creativity by suggesting narratives.
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning
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 induced in multi-agent settings.
Introspective Action Advising for Interpretable Transfer Learning
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 source task.
Sample-Efficient Learning of Novel Visual Concepts
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 limited number of examples are provided.
Concept Learning for Interpretable Multi-Agent Reinforcement Learning
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 neural network representations.
Predicting Out-of-Distribution Error with Confidence Optimal Transport
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops.
Liquid Democracy. Two Experiments on Delegation in Voting
In addition, subjects substantially overestimate the precision of the better informed voters, underlining that Liquid Democracy is fragile to multiple sources of noise.
Learning and Blending Robot Hugging Behaviors in Time and Space
We introduce an imitation learning-based physical human-robot interaction algorithm capable of predicting appropriate robot responses in complex interactions involving a superposition of multiple interactions.
Explainable Action Advising for Multi-Agent Reinforcement Learning
This allows the student to self-reflect on what it has learned, enabling advice generalization and leading to improved sample efficiency and learning performance - even in environments where the teacher is sub-optimal.
Multi-agent Reinforcement Learning
reinforcement-learning
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Learning Predictive Models for Ergonomic Control of Prosthetic Devices
We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanical impact on the human musculoskeletal system.
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Imitation learning is a popular approach for teaching motor skills to robots.
Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking
We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior.
Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts
This paper presents a learning-from-demonstration (LfD) framework for teaching human-robot social interactions that involve whole-body haptic interaction, i. e. direct human-robot contact over the full robot body.
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration
In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time.
Imitation Learning of Robot Policies using Language, Vision and Motion
In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn can be used to synthesize specific motion controllers at run-time.
Multimodal Dataset of Human-Robot Hugging Interaction
33 people were given minimal instructions to hug the humanoid robot for as natural hugging interaction as possible.
Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives
Musculoskeletal robots that are based on pneumatic actuation have a variety of properties, such as compliance and back-drivability, that render them particularly appealing for human-robot collaboration.
Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks
Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy.
