32 citations · 44 across the 9 of their papers we have counts for
9 papers
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, can recent advances in agentic coding systems combine interpretable robot programming with the open-world ada…
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…
Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool
Suzan Ece Ada, M. Yunus Seker
Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, Generative Adversarial Networks with Conditiona…
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…