15 accepted papers
Robotic assembly presents a long-standing challenge due to its requirement for precise, contact-rich manipulation. While simulation-based learning has enabled the development of robust assembly policies, their performance often degrades when deployed in real-world settings due to the sim-to-real gap…
Insertion is an essential skill for robots in both modern manufacturing and services robotics. In our previous study, we proposed an insertion skill framework based on forcedomain wiggle motion. The main limitation of this method lies in the robot’s inability to adjust its behavior according to chan…
The intersection of robotics and artificial intelligence led to a profound paradigm shift in Robot Learning. Robots have the capacity to replicate human actions and also dynamically adapt, innovate, and excel across a spectrum of tasks. However, the heterogeneity in the deployment of robot platforms…
The adaption of robotic assembly lines to new products is generally slow and costly, binding the economical usage of a single line to a few products manufactured in masses. An important time factor is the adaption of the assembly robot's gripper fingers to the new product components - since finger d
Reaching human-level performance in tactile manipulation is one of the grand challenges in nowadays robotics research. Over the past decade significant progress in both skill control and learning was made. However, the achievable execution speed still falls behind the human ability, without clearly…
Teleoperated robots are believed to play an important role for future applications in industry, medicine and other domains. Examples for this are remote assembly and maintenance, surgery, diagnosis or deep-sea and space exploration. Such applications are made possible by state-of-the-art tactile man…
The current crisis surrounding the COVID-19 pandemic demonstrates the amount of responsibility and the workload on our healthcare system and, above all, on the medical staff around the world. In this work, we propose a promising approach to overcome this problem using robot-assisted telediagnostics,
In this paper we apply a multi-level structure to robotic manipulation learning. It consists of a hybrid dynamical system we denote skill and a parameter learning layer that leverages the underlying structure to simplify the problem at hand. For the learning layer we introduce a novel algorithm base
Ideally, a robot controller should not only be designed to exhibit a given interaction behavior under controlled conditions, but also to be robust to changes e.g. in the environment. Within the paradigm of virtual energy tanks for passivity-based controls, robustness may be provided by setting absol
In this paper we introduce a novel framework for expressing and learning force-sensitive robot manipulation skills. It is based on a formalism that extends our previous work on adaptive impedance control with meta parameter learning and compatible skill specifications. This way the system is also ab…
In this paper we introduce dentronics as a new emerging application domain for collaborative lightweight robots in the dental context backed up by a user survey supporting the clear need. Specifically, we developed a multi-modal interaction framework, applied this framework to a specific dental use-…
In this paper the first robot-based prototype of a semi-autonomous upper-limb exoprosthesis is introduced, unifying exoskeletons and prostheses [1]. A central goal of this work is to minimize unnecessary interaction forces on the residual limb by compensating gravity effects via a upper body grounde…
In this paper we introduce a novel point-to-point trajectory planner for serial robotic structures that combines the ability to avoid self-collisions and to respect motion constraints, while complying with the requirement of being C4 continuous. The latter property makes our approach also suited for…
Performing skillfull manipulation is a very challenging task for robots. So far, even experts could barely program them to e.g. perform the well known peg-in-hole problem in the real world. Autonomously acquiring such skills, let alone generalizing them to new tasks, is still a major challenge. Typi…
In this letter, we propose a framework for task allocation in human-robot collaborative assembly planning. Our framework distinguishes between two main layers of abstraction and allocation. In the higher layer, we use an abstract world model, incorporating a multiagent human-robot team approach in o