95 citations · 260 across the 32 of their papers we have counts for
7 papers · 1 filter
Correspondence learning between morphologically different robots via task demonstrations
Hakan Aktas, Yukie Nagai, Minoru Asada +2
We observe a large variety of robots in terms of their bodies, sensors, and actuators. Given the commonalities in the skill sets, teaching each skill to each different robot indepe…
Discovering Predictive Relational Object Symbols with Symbolic Attentive Layers
Alper Ahmetoglu, Batuhan Celik, Erhan Oztop +1
In this paper, we propose and realize a new deep learning architecture for discovering symbolic representations for objects and their relations based on the self-supervised continu…
Multi-Object Graph Affordance Network: Goal-Oriented Planning through Learned Compound Object Affordances
Tuba Girgin, Emre Ugur
Learning object affordances is an effective tool in the field of robot learning. While the data-driven models investigate affordances of single or paired objects, there is a gap in…
Developmental Scaffolding with Large Language Models
Batuhan Celik, Alper Ahmetoglu, Emre Ugur +1
Exploratoration and self-observation are key mechanisms of infant sensorimotor development. These processes are further guided by parental scaffolding accelerating skill and knowle…
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning
Suzan Ece Ada, Erhan Oztop, Emre Ugur
Offline Reinforcement Learning (RL) methods leverage previous experiences to learn better policies than the behavior policy used for data collection. However, they face challenges…
Multi-step planning with learned effects of partial action executions
Hakan Aktas, Utku Bozdogan, Emre Ugur
In this paper, we propose a novel affordance model, which combines object, action, and effect information in the latent space of a predictive neural network architecture that is bu…
Meta-World Conditional Neural Processes
Suzan Ece Ada, Emre Ugur
We propose Meta-World Conditional Neural Processes (MW-CNP), a conditional world model generator that leverages sample efficiency and scalability of Conditional Neural Processes to…