research
∙
09/19/2023
Multi-Object Graph Affordance Network: Enabling Goal-Oriented Planning through Compound Object Affordances
Learning object affordances is an effective tool in the field of robot l...
share
research
∙
09/02/2023
Developmental Scaffolding with Large Language Models
Exploratoration and self-observation are key mechanisms of infant sensor...
research
∙
09/02/2023
Discovering Predictive Relational Object Symbols with Symbolic Attentive Layers
In this paper, we propose and realize a new deep learning architecture f...
research
∙
07/10/2023
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning
Offline Reinforcement Learning (RL) methods leverage previous experience...
research
∙
03/16/2023
Multi-step planning with learned effects of (possibly partial) action executions
In this paper, we propose an affordance model, which is built on Conditi...
research
∙
02/20/2023
Meta-World Conditional Neural Processes
We propose Meta-World Conditional Neural Processes (MW-CNP), a condition...
research
∙
01/14/2023
World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges
Creating autonomous robots that can actively explore the environment, ac...
research
∙
10/07/2022
Learning Social Navigation from Demonstrations with Conditional Neural Processes
Sociability is essential for modern robots to increase their acceptabili...
research
∙
09/28/2022
Bimanual rope manipulation skill synthesis through context dependent correction policy learning from human demonstration
Learning from demonstration (LfD) provides a convenient means to equip r...
research
∙
08/01/2022
Learning Multi-Object Symbols for Manipulation with Attentive Deep Effect Predictors
In this paper, we propose a concept learning architecture that enables a...
research
∙
06/18/2021
High-level Features for Resource Economy and Fast Learning in Skill Transfer
Abstraction is an important aspect of intelligence which enables agents ...
research
∙
02/03/2021
Object and Relation Centric Representations for Push Effect Prediction
Pushing is an essential non-prehensile manipulation skill used for tasks...
research
∙
12/04/2020
DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning
Autonomous discovery of discrete symbols and rules from continuous inter...
research
∙
11/09/2020
Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization
The aim of this paper is to study the reward based policy exploration pr...
research
∙
08/26/2020
Intrinsic Motivation in Object-Action-Outcome Blending Latent Space
One effective approach for equipping artificial agents with sensorimotor...
research
∙
07/26/2020
Trick the Body Trick the Mind: Avatar representation affects the perception of available action possibilities in Virtual Reality
In immersive Virtual Reality (VR), your brain can trick you into believi...
research
∙
07/23/2020
Time Perception: A Review on Psychological, Computational and Robotic Models
Animals exploit time to survive in the world. Temporal information is re...
research
∙
03/25/2020
Adaptive Conditional Neural Movement Primitives via Representation Sharing Between Supervised and Reinforcement Learning
Learning by Demonstration provides a sample efficient way to equip robot...
research
∙
09/09/2019
Belief Regulated Dual Propagation Nets for Learning Action Effects on Articulated Multi-Part Objects
In recent years, graph neural networks have been successfully applied fo...
research
∙
09/03/2019
Generalization in Transfer Learning
Agents trained with deep reinforcement learning algorithms are capable o...
research
∙
01/26/2018
