Co-bots and physical HRI

As robots are leaving safety fences and starting to share workspaces and even living spaces with humans, they need to dynamically adapt to unpredictable interactions with people and guarantee safety at every moment. On the rapidly growing market for collaborative robots, safety is ensured through specific technologies such as force limitations by design or contact detection and stopping relying on force measurements. Humans, however, possess whole-body awareness drawing on dynamic, context-dependent fusion of multimodal sensory information, which makes them adaptive, flexible, and versatile. 

In my research, I strive to add important new dimensions to physical human-robot interaction. The first goal is to make the robot aware of its whole body rather than the end-effector only. We use artificial electronic skins to cover the whole robot's body - this forms the basis for its "whole-body awareness".  Second, inspired by the way the brain represents the body, we strive to make these representations multimodal. Using vision, or audition, the body space can be extended to a surface around the body (so-called peripersonal space), facilitating collision avoidance and contact anticipation, eventually leading to safer and more natural interaction of the robot with objects, including humans.

Effect of active and passive protective soft skins on collision forces in HRI

Svarny, P., Rozlivek, J., Rustler, L., Sramek, M., Deli, Ö., Zillich, M. and Hoffmann, M. (2022), 'Effect of active and passive protective soft skins on collision forces in human–robot collaboration', Robotics and Computer-Integrated Manufacturing 78, 102363. [DOI - sciencedirect][arxiv-pdf][dataset][youtube-video]

3D Collision-Force-Map - Empirical measurement and data-drive modeling of impact forces warrants safety and boosts performance

Svarny, P.; Rozlivek, J.; Rustler, L. & Hoffmann, M. (2021), '3D Collision-Force-Map for Safe Human-Robot Collaboration', IEEE International Conference on Robotics and Automation (ICRA), 3829-3835. [IEEE Xplore][pdf-arxiv][youtube-video]

Speed and separation monitoring together with power and force limiting (per ISO/TS 15066) regimes in a single collaborative robot scenario

Svarny, P.; Tesar, M.; Behrens, J. K. & Hoffmann, M. (2019), Safe physical HRI: Toward a unified treatment of speed and separation monitoring together with power and force limiting, in 'Intelligent Robots and Systems (IROS), 2019 IEEE/RSJ International Conference on', IEEE, pp. 7574-7581. [IEEE Xplore][arxiv][youtube video]

Robot speed modulation for safe Human-Robot Collaboration

Zardykhan, D.; Svarny, P.; Hoffmann, M.; Shahriari, E. & Haddadin, S. (2019), Collision Preventing Phase-Progress Control for Velocity Adaptation in Human-Robot Collaboration, in 'Humanoid Robots (Humanoids), 2019 IEEE-RAS 18th International Conference on', IEEE, pp. 282-289.[IEEE Xplore]                                                                    [youtube video]

Work in collaboration with Sami Haddadin's group from Technical University of Munich.

Reaching with anticipatory avoidance of human using skeleton extraction

Work in collaboration with IIT Genoa and Yale University.

Nguyen, P. D.; Hoffmann, M.; Roncone, A.; Pattacini, U. & Metta, G. (2018), Compact real-time avoidance on a humanoid robot for human-robot interaction, in 'HRI ’18: 2018 ACM/IEEE International Conference on Human-Robot Interaction', ACM, New York, NY, USA, pp. 416-424. [ACM digital library][arxiv][youtube video]

Learning peripersonal space representation from visuo-tactile association

The goal is to extend the model of the body itself to the space surrounding it, while preserving the key role of the tactile modality. The first step is thus acquiring a representation of this so-called peripersonal space.  In a second step, contacts over the whole body surface will be perceived and can be handled by the robot depending on the task – they can be kept within limits or they can be sought. This will greatly increase the range of configurations available to reach in cluttered spaces, while ensuring safety for the robot itself as well as its environment at all times.

Roncone, A.; Hoffmann, M.; Pattacini, U.; Fadiga, L. & Metta, G. (2016), 'Peripersonal space and margin of safety around the body: learning tactile-visual associations in a humanoid robot with artificial skin', PLoS ONE 11(10), e0163713. [OPEN ACCESS - doi link]

Roncone, A.; Hoffmann, M.; Pattacini, U. & Metta, G. (2015), Learning peripersonal space representation through artificial skin for avoidance and reaching with whole body surface, in 'Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on', pp. 3366-3373. [IEEE Xplore] [postprint]