A key achievement has been the development of a real-time, personalized human kinodynamics models, enabling precise quantification of motor capacity, ergonomic factors, and metabolic costs. This framework identified overloading joint torques and external contact forces, leading to validated models that improve ergonomic assessments. Using these models, an advanced control framework was developed for collaborative robots, integrating priority-based motion control to optimize human ergonomics in co-manipulation and teleoperation.
The project also introduced novel Ergo-Assistant interfaces, including real-time graphical feedback and vibrotactile guidance, effectively minimizing ergonomic risks. Shared authority models optimized task allocation in HRC, distributing actions based on ergonomic risk and human capabilities, enhancing efficiency while reducing strain. Adaptive HRI frameworks enabled robots to adjust behavior based on cognitive and physical workload using reinforcement learning and optimization techniques. A major breakthrough was the development of a sustainable, biodegradable, solar-powered wearable device for ergonomic feedback. Additionally, a versatile hierarchical control framework was created for various HRI scenarios, eliminating the need for multiple architectures and enabling seamless transitions between interaction modes.
Kinesthetic feedback-driven robotic assistance was another key innovation, where robots provided real-time resistance to non-ergonomic postures, fostering long-term ergonomic habits. User studies, electromyography analyses, and industrial trials confirmed significant reductions in physical and cognitive workload. The project’s impact was widely disseminated through high-impact publications, keynote presentations at leading robotics conferences, and extensive media coverage. The team organized workshops and scientific events, further contributing to human-centric robotic systems. The project's achievements were recognized through prestigious awards, including the IEEE Robotics and Automation Society Early Career Award.