4 papers · 1 filter
Neuro-Symbolic Skill Discovery for Conditional Multi-Level Planning
Hakan Aktas, Yigit Yildirim, Ahmet Firat Gamsiz +3
This paper proposes a novel learning architecture for acquiring generalizable high-level symbolic skills from a few unlabeled low-level skill trajectory demonstrations. The archite…
Cross-Embodied Affordance Transfer through Learning Affordance Equivalences
Hakan Aktas, Yukie Nagai, Minoru Asada +3
Affordances represent the inherent effect and action possibilities that objects offer to the agents within a given context. From a theoretical viewpoint, affordances bridge the gap…
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…
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…