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.

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