Prof. Dr. Philipp Beckerle
Chair of Autonomous Systems and Mechatronics

Prof. Dr. Philipp Beckerle
Chair of Autonomous Systems and Mechatronics

We develop technical systems that functionally support their users and provide them with a positive experience.Our research approach equally considers human factors and technical requirements based on a mixture of methods from engineering and human sciences. We demonstrate our findings on wearable systems such as prostheses or exoskeletons, cognitive systems such as collaborative or humanoid robots, and general applications with tight human-robot interaction.

Research projects

  • Fault diagnosis and tolerance for elastic actuation systems in robotics: physical human-robot interaction
  • Active transfer learning with neural networks through human-robot interactions (TRAIN)
  • EFFENDI – EFficient and Fast text ENtry for persons with motor disabilities of neuromuscular origin

  • Synergy-based model predictive interaction control for robotic hand-arm systems

    (Third Party Funds Single)

    Project leader: ,
    Term: 1. January 2026 - 31. December 2028
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    Abstract

    Robotic hands are often motivated by the fact that they can be used to perform a wide range of manipulation tasks. The extension to a robotic hand-arm system (e.g., as part of a humanoid robot) makes it possible to significantly reduce the workload of people in shared workspaces. Clear examples of this can be found both in industrial manufacturing processes (pick and place, assembly, machining, etc.) and in everyday human life (opening doors, cutting food, handling objects, etc.). In addition to complicated movements, such tasks require the precise adjustment of forces. Nevertheless, they are usually performed by humans or various highly specialized machines. Alternatively, the environment could be adapted to the technology, but this is hardly practicable. In addition to safety, a particular challenge in shared workspaces is the acceptance of the robot system by the people present. An important aspect of this is that people must be able to recognize and anticipate the robot's movements intuitively. The research project aims to develop a synergy-based model predictive control (MPC) for robotic hand-arm systems that is integrated into a modular control architecture. The challenges of numerous actuated degrees of freedom are considered and the methods for null-space control are co-developed. With the help of a model-predictive approach, the aim is to achieve a modular architecture that also integrates task planning. Various approaches are being developed to efficiently integrate linear and non-linear kinematic synergies into a modular MPC formulation. Therein, the synergies are intended to reduce the complexity and thus the computation effort on the one hand and to realize human-like motions on the other. This approach also aims to increase the human acceptance of such systems.

  • Gewebe-Regenerator: Adaptive und automatisierte Geweberegeneration am Skelettmuskel mit dem Tissue Regenerator (Gewebe-Regenerator)

    (Third Party Funds Single)

    Project leader: , , ,
    Term: 1. April 2025 - 31. March 2028
    Acronym: Gewebe-Regenerator
    Funding source: Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
    URL: https://www.mbt.tf.fau.de/research/research-groups/bioreactors-in-tissue-engineering/the-myoreactor-prototype/
    Abstract

    Auf 1 Mio. in Deutschland lebende Menschen kommen gerade einmal 11 Organspender und Organspenderinnen. Dies waren die ernüchternden Ergebnisse einer 2020 durchgeführten Studie der BzgA. Besonders bei traumatischen Verletzungen der Skelettmuskulatur durch, z.B. einen Autounfall, Beißattacken, Schuss-, oder Explosionsverletzungen, besteht bislang keine Perspektive für eine vollständige Wiederherstellung der ursprünglichen Funktion. Grund hierfür ist, dass eine Fremdtransplantation beim Empfänger mit einem starken Volumen- und damit Kraftverlust verbunden ist. Eigentransplantationen schädigen wiederum die Stelle der Entnahme nachhaltig. In vitro hergestellte Organe sind von diesen Einschränkungen entkoppelt und öffnen ganz neue Behandlungsmöglichkeiten. Leider schwanken derart hergestellte Organe aufgrund der begrenzten Technologien im Bereich des Tissue Engineering und vielen manuellen Arbeitsschritten noch sehr stark in ihrer finalen Qualität und Funktionalität. Besonders bisherige invasive Qualitätsanalysen (z.B. Dünnschnitte) verhindern die weitere Verwertung des Präparats. Dies geschieht meist schon während der Herstellung der zellfreien Kollagenmatrizen - dem Ausgangspunkt für eine Wiederbesiedlung mit Spenderzellen (in der Abbildung links im Bioreaktor dargestellt). Mit dem Validierungsprojekt zielen wir darauf ab, den Prozess von der Herstellung zellfreier Kollagenmatrizen bis hin zum regenerierten Skelettmuskel zu automatisieren und neue nicht-invasive Parameter als Qualitätsstandard zu etablieren. Danach planen wir sowohl die Vermarktung der Plattform als auch die Dienstleistung der Regeneration in einer Ausgründung zu integrieren.

  • präFlott: Learning Predictive Maintenance of Fleets of Networked Systems

    (Third Party Funds Single)

    Project leader:
    Term: 1. September 2023 - 31. August 2026
    Acronym: präFlott
    Funding source: Bayerische Forschungsstiftung
    Abstract

    The project aims at advancing predictive maintenance for networked device fleets using learning approaches and integrating expert knowledge. To this end, we will combine machine learning with physical models and analyze data flows between systems as well as integrate expertise on system behavior and failure modes. The resulting predictive maintenance approach for networked systems will be transferred to various classes of systems. Besides investigating industrial applications, we will create a fleet of mobile robots to demonstrate the capabilities of the predictive maintenance approach and make it available to academia, industry, and beyond.

2026

2025

2024

2023

2022

2021

2020

Related Research Fields