32 citations · 44 across the 9 of their papers we have counts for
8 papers · 1 filter
GTA-2: A Multi-VLM Framework for Synthesizing Robot Manipulation Skills via Grounded Task Axes
M. Yunus Seker, Shobhit Aggarwal, Ruwan Wickramarachchi +2
Robotic manipulation tasks are often decomposed into behaviors or skills. However, one often needs to predefine these behaviors for specific tasks or try to cover a wide range of t…
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
Kaiyuan Chen, Shuangyu Xie, Letian Fu +21
For robots to work reliably in commercial and industrial applications, recent advances in agentic coding systems have potential to combine interpretable robot programming with the…
Grounded Task Axes: Zero-Shot Semantic Skill Generalization via Task-Axis Controllers and Visual Foundation Models
M. Yunus Seker, Shobhit Aggarwal, Oliver Kroemer
Transferring skills between different objects remains one of the core challenges of open-world robot manipulation. Generalization needs to take into account the high-level structur…
Leveraging Simulation-Based Model Preconditions for Fast Action Parameter Optimization with Multiple Models
M. Yunus Seker, Oliver Kroemer
Optimizing robotic action parameters is a significant challenge for manipulation tasks that demand high levels of precision and generalization. Using a model-based approach, the ro…
Estimating Material Properties of Interacting Objects Using Sum-GP-UCB
M. Yunus Seker, Oliver Kroemer
Robots need to estimate the material and dynamic properties of objects from observations in order to simulate them accurately. We present a Bayesian optimization approach to identi…
Imitation and Mirror Systems in Robots through Deep Modality Blending Networks
M. Y. Seker, A. Ahmetoglu, Y. Nagai +3
Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and…
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…