8 accepted papers
Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited data. Combining these approaches holds significant promise for direct application to robotics, yet this combination has rec
Learning from demonstration (LfD) offers an intuitive alternative to manual coding by leveraging natural human behavior, while Human-Robot Interaction (HRI) provides an intuitive means to refine and adapt learned skills. Among interaction modalities, physical contact is a natural and effective way t
Learning from Demonstration (LfD) is a widely used approach for teaching robot motion, but more sophisticated strategies are required to address complex tasks such as surface processing. Sanding is an example where comprehensive strategies are necessary to ensure complete and efficient coverage of t
Surface finishing such as grinding or polishing is a time-consuming task, involves health risks for humans and is still largely performed by hand. Due to the high curvatures of complex geometries, different areas of the surface cannot be optimally reached by a simple strategy using a tool with a rel
It lies in human nature to properly adjust the muscle force to perform a given task successfully. While transferring this control ability to robots has been a big concern among researchers, there is no attempt to make a robot learn how to control the impedance solely based on visual observations. Ra
Conventional robot programming methods are not suited for non-experts to intuitively teach robots new tasks. For this reason, the potential of collaborative robots for production cannot yet be fully exploited. In this work, we propose an active learning framework, in which the robot and the user col…
Conditional tasks include a decision on how the robot should react to an observation. This requires to select the appropriate action during execution. For instance, spatial sorting of objects may require different goal positions based on the objects properties, such as weight or geometry. We propose
The recent generation of compliant robots enables kinesthetic teaching of novel skills by human demonstration. This enables strategies to transfer tasks to the robot in a more intuitive way than conventional programming interfaces. Programming physical interactions can be achieved by manually guidin…