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Found 11 papers, 2 papers with code
Learning Action-based Representations Using Invariance
Robust reinforcement learning agents using high-dimensional observations must be able to identify relevant state features amidst many exogeneous distractors.
Feedback Efficient Online Fine-Tuning of Diffusion Models
It is natural to frame this as a reinforcement learning (RL) problem, in which the objective is to fine-tune a diffusion model to maximize a reward function that corresponds to some property.
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins.
BridgeData V2: A Dataset for Robot Learning at Scale
By publicly sharing BridgeData V2 and our pre-trained models, we aim to accelerate research in scalable robot learning methods.
ViNT: A Foundation Model for Visual Navigation
In this paper, we describe the Visual Navigation Transformer (ViNT), a foundation model that aims to bring the success of general-purpose pre-trained models to vision-based robotic navigation.
Granger-Causal Hierarchical Skill Discovery
Reinforcement Learning (RL) has demonstrated promising results in learning policies for complex tasks, but it often suffers from low sample efficiency and limited transferability.
Training Diffusion Models with Reinforcement Learning
However, most use cases of diffusion models are not concerned with likelihoods, but instead with downstream objectives such as human-perceived image quality or drug effectiveness.
Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery
A key requirement for autonomous on-orbit proximity operations is the estimation of a target spacecraft's relative pose (position and orientation).
A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery
Firstly, efficient implementation of these techniques relies on an effective system for model development that streamlines data curation, training, and evaluation.
Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage
The task of corpus-dictionary linkage (CDL) is to annotate each word in a corpus with a link to an appropriate dictionary entry that documents the sense and usage of the word.
