My group's vision is to have physically intelligent robots that augment rather than replace humans in a safe, personalised, and ergonomic manner. Two overarching research questions emerge: 1) how to transfer physical interaction skills to robots, and 2) how to ensure the interaction is personalised and ergonomic? To address these research questions, our approach is to include humans in the control loop of robotic systems for real-time control of complex physical interactions in unstructured and unpredictable environments. The group employs user-centred design to create new interfaces for human-in-the-loop physical skill transfer and encodes these skills with statistical machine learning to capture the stochasticity of human behaviour. For personalised and ergonomic assistance, we incorporate high-fidelity human musculoskeletal and motor control models to inform the interaction controller that is based on adaptive impedance control.
Davide Torielli
(Postdoc)
Teleoperation Shared-Control Robot Learning
Nicky Mol
(PhD Candidate)
Meaningful Roles in Physical Human-Robot Collaboration
Guiomar Cudell Santos Carvalho (PhD Candidate)
Biomechanics-aware Robotic Surgery and Physiotherapy
Stij Klevering
(PhD Candidate)
Biomechanics-aware Robotic Surgery and Physiotherapy
Alejandro Díaz Rosales
(PhD Candidate)
Teleoperated Robotic Maintance at CERN
Arwin Hidding
(PhD Candidate)
Robot-assisted Construction of Off-Earth Habitats
Italo Belli
Obtained PhD with thesis: Biomechanics-aware Control for Robot-assisted Physiotherapy: A Novel Approach to Treating Shoulder Injuries
Giovanni Franzese
Obtained PhD with thesis: Uncertainty-aware Interactive Imitation Learning for Robot Manipulation