11 citations · 31 across the 9 of their papers we have counts for
9 papers
Compositional Motion Generation from Demonstration with Object-Centric Neural Fields
Ahmet Ercan Tekden, Yasemin Bekiroglu
Compositionality, by organizing complex behavior as combinations of simpler elements, enables robot learning that is scalable and data efficient. Leveraging this principle, we prop…
Reactive Motion Generation via Phase-varying Neural Potential Functions
Ahmet Tekden, Dimitrios Kanoulas, Aude Billard +1
Dynamical systems (DS) methods for Learning-from-Demonstration (LfD) provide stable, continuous policies from few demonstrations. First-order dynamical systems (DS) are effective f…
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces
Ahmet Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu
Objects we interact with and manipulate often share similar parts, such as handles, that allow us to transfer our actions flexibly due to their shared functionality. This work addr…
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
Ahmet Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu
This paper presents a novel Learning from Demonstration (LfD) method that uses neural fields to learn new skills efficiently and accurately. It achieves this by utilizing a shared…
Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-fingered Hands
Yiting Chen, Ahmet Ercan Tekden, Marc Peter Deisenroth +1
Efficient and accurate 3D object shape reconstruction contributes significantly to the success of a robot's physical interaction with its environment. Acquiring accurate shape info…
Object and Relation Centric Representations for Push Effect Prediction
Ahmet E. Tekden, Aykut Erdem, Erkut Erdem +2
Pushing is an essential non-prehensile manipulation skill used for tasks ranging from pre-grasp manipulation to scene rearrangement, reasoning about object relations in the scene,…
Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization
M. Tuluhan Akbulut, Utku Bozdogan, Ahmet Tekden +1
The aim of this paper is to study the reward based policy exploration problem in a supervised learning approach and enable robots to form complex movement trajectories in challengi…
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing
M. Tuluhan Akbulut, Erhan Oztop, M. Yunus Seker +3
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exp…
Belief Regulated Dual Propagation Nets for Learning Action Effects on Groups of Articulated Objects
Ahmet E. Tekden, Aykut Erdem, Erkut Erdem +3
In recent years, graph neural networks have been successfully applied for learning the dynamics of complex and partially observable physical systems. However, their use in the robo…