A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910274344189952 |
|---|---|
| author | Mühlbauer, Maximilian Weber, Bernhard Calinon, Sylvain Stulp, Freek Albu-Schäffer, Alin Silvério, João |
| author_facet | Mühlbauer, Maximilian Weber, Bernhard Calinon, Sylvain Stulp, Freek Albu-Schäffer, Alin Silvério, João |
| contents | Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While keeping the human in the loop remains essential, for instance, to ensure high precision, partial automation of certain task phases is critical for productivity. We present a unified framework for probabilistic VFs that seamlessly switches between manual fixtures, semi-automated fixtures (with the human handling precise tasks), and full autonomy. We introduce a novel probabilistic Dynamical System-based VF for coarse guidance, enabling the robot to autonomously complete certain task phases while keeping the human operator in the loop. For tasks requiring precise guidance, we extend probabilistic position-based trajectory fixtures with automation, allowing for seamless human interaction, geometry-awareness and optimal impedance gains. For manual tasks requiring very precise guidance, we also extend visual servoing fixtures with the same geometry-awareness and impedance behavior. We validate our approach on different robots, including an evaluation with expert users, showcasing operation modes, the ease of programming fixtures and lower interaction forces and favorable usability compared to a baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10239 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures Mühlbauer, Maximilian Weber, Bernhard Calinon, Sylvain Stulp, Freek Albu-Schäffer, Alin Silvério, João Robotics Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While keeping the human in the loop remains essential, for instance, to ensure high precision, partial automation of certain task phases is critical for productivity. We present a unified framework for probabilistic VFs that seamlessly switches between manual fixtures, semi-automated fixtures (with the human handling precise tasks), and full autonomy. We introduce a novel probabilistic Dynamical System-based VF for coarse guidance, enabling the robot to autonomously complete certain task phases while keeping the human operator in the loop. For tasks requiring precise guidance, we extend probabilistic position-based trajectory fixtures with automation, allowing for seamless human interaction, geometry-awareness and optimal impedance gains. For manual tasks requiring very precise guidance, we also extend visual servoing fixtures with the same geometry-awareness and impedance behavior. We validate our approach on different robots, including an evaluation with expert users, showcasing operation modes, the ease of programming fixtures and lower interaction forces and favorable usability compared to a baseline. |
| title | A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.10239 |