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Found 33 papers, 17 papers with code
Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction
A principal issue is that in prior methods, the KG schema has to be included in the LLM prompt to generate valid triplets; larger and more complex schema easily exceed the LLMs' context window length.
Behavioral Refinement via Interpolant-based Policy Diffusion
However, the target policy to be learned is often significantly different from Gaussian and this mismatch can result in poor performance when using a small number of diffusion steps (to improve inference speed) and under limited data.
Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation
To advance the field of autonomous robotics, particularly in object search tasks within unexplored environments, we introduce a novel framework centered around the Probable Object Location (POLo) score.
Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction
Perspective-taking is the ability to perceive or understand a situation or concept from another individual's point of view, and is crucial in daily human interactions.
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities
There is increasing attention being given to how to regulate AI systems.
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content.
Generative Modeling with Flow-Guided Density Ratio Learning
We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale (time-independent) approximation of the gradient flow of entropy-regularized f-divergences introduced in DGflow.
Large Language Models as Zero-Shot Human Models for Human-Robot Interaction
In this work, we explore the potential of large-language models (LLMs) -- which have consumed vast amounts of human-generated text data -- to act as zero-shot human models for HRI.
Translating Natural Language to Planning Goals with Large-Language Models
Recent large language models (LLMs) have demonstrated remarkable performance on a variety of natural language processing (NLP) tasks, leading to intense excitement about their applicability across various domains.
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Learning accurate predictive models of real-world dynamic phenomena (e. g., climate, biological) remains a challenging task.
Safety-Constrained Policy Transfer with Successor Features
In this work, we focus on the problem of safe policy transfer in reinforcement learning: we seek to leverage existing policies when learning a new task with specified constraints.
Observed Adversaries in Deep Reinforcement Learning
In this work, we point out the problem of observed adversaries for deep policies.
SCALES: From Fairness Principles to Constrained Decision-Making
This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (CMDP).
MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication
In this work, we present MIRROR, an approach to (i) quickly learn human models from human demonstrations, and (ii) use the models for subsequent communication planning in assistive shared-control settings.
Deep Explicit Duration Switching Models for Time Series
We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent switching dynamics.
Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning
This work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors.
Extended Tactile Perception: Vibration Sensing through Tools and Grasped Objects
Humans display the remarkable ability to sense the world through tools and other held objects.
Embedding Symbolic Temporal Knowledge into Deep Sequential Models
However, when data is limited, simpler models such as logic/rule-based methods work surprisingly well, especially when relevant prior knowledge is applied in their construction.
Refining Deep Generative Models via Discriminator Gradient Flow
We introduce Discriminator Gradient flow (DGflow), a new technique that improves generated samples via the gradient flow of entropy-regularized f-divergences between the real and the generated data distributions.
Ranked #1 on
Text Generation
on One Billion Word
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration.
Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration
Common experience suggests that agents who know each other well are better able to work together.
TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition
In this paper, we propose a novel spiking graph neural network for event-based tactile object recognition.
The Evolutionary Dynamics of Independent Learning Agents in Population Games
Understanding the evolutionary dynamics of reinforcement learning under multi-agent settings has long remained an open problem.
ST-MNIST -- The Spiking Tactile MNIST Neuromorphic Dataset
We anticipate that our ST-MNIST dataset will be of interest and useful to the neuromorphic and robotics research communities.
A Characteristic Function Approach to Deep Implicit Generative Modeling
In this paper, we formulate the problem of learning an IGM as minimizing the expected distance between characteristic functions.
Robot Capability and Intention in Trust-based Decisions across Tasks
In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots---inferred capability and intention---and their relationship to overall trust and eventual decisions.
Embedding Symbolic Knowledge into Deep Networks
Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding.
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
Integrating deep learning with latent state space models has the potential to yield temporal models that are powerful, yet tractable and interpretable.
Applying Probabilistic Programming to Affective Computing
Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models.
Hyperprior Induced Unsupervised Disentanglement of Latent Representations
We address the problem of unsupervised disentanglement of latent representations learnt via deep generative models.
Generation Meets Recommendation: Proposing Novel Items for Groups of Users
Who would the movies appeal to?
Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning
The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term.
Probabilistic Network Metrics: Variational Bayesian Network Centrality
The sparse variational Bayesian centrality Gaussian process (VBC-GP) learns a mapping between node attributes to latent centrality and hence, is capable of predicting centralities from node features and can potentially represent a large number of nodes using only a limited number of inducing inputs.
