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Found 14 papers, 4 papers with code
Offline Actor-Critic Reinforcement Learning Scales to Large Models
We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning.
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples for the target task.
Unlocking the Power of Representations in Long-term Novelty-based Exploration
We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space.
Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration
Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks.
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects.
Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives
Visuomotor control (VMC) is an effective means of achieving basic manipulation tasks such as pushing or pick-and-place from raw images.
Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis
In this paper we explore the richness of information captured by the latent space of a vision-based generative model.
Imagine That! Leveraging Emergent Affordances for Tool Synthesis in Reaching Tasks
In this paper we investigate an artificial agent's ability to perform task-focused tool synthesis via imagination.
Guiding Physical Intuition with Neural Stethoscopes
Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%.
Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes
Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset.
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Physical intuition is pivotal for intelligent agents to perform complex tasks.
Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation
Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state of the state-of-the-art results for depth estimation and semantic labeling.
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering.
Visual7W: Grounded Question Answering in Images
It enables a new type of QA with visual answers, in addition to textual answers used in previous work.
Multiple-choice
Multiple Choice Question Answering (MCQA)
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