6 papers · 1 filter
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…
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…
Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration
Yigit Yildirim, Emre Ugur
Learning from Demonstration (LfD) is a widely used technique for skill acquisition in robotics. However, demonstrations of the same skill may exhibit significant variances, or lear…
Bidirectional Human Interactive AI Framework for Social Robot Navigation
Tuba Girgin, Emre Girgin, Yigit Yildirim +2
Trustworthiness is a crucial concept in the context of human-robot interaction. Cooperative robots must be transparent regarding their decision-making process, especially when oper…
Learning Early Social Maneuvers for Enhanced Social Navigation
Yigit Yildirim, Mehmet Suzer, Emre Ugur
Socially compliant navigation is an integral part of safety features in Human-Robot Interaction. Traditional approaches to mobile navigation prioritize physical aspects, such as ef…
Learning Social Navigation from Demonstrations with Deep Neural Networks
Yigit Yildirim, Emre Ugur
Traditional path-planning techniques treat humans as obstacles. This has changed since robots started to enter human environments. On modern robots, social navigation has become an…