Learning Human-like Trajectories for Whole-Body Motion with Artificial Force Fields (C03)

• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2020 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 416228727

Presumably in hybrid societies, robots will only be accepted if their motions are predictable and human-like. Therefore, the project aims to optimize robot trajectories in terms of human-likeness. New methods will be developed for generating collision-free and functional trajectories by applying an end-to-end learned deep neural network, which uses depth maps. Deep neuronal networks are trained autonomously by traditional sampling-based motion planning methods. Moreover, a deep reinforcement learning approach adapts robot trajectories with artificial force fields such that they increase human-likeness. Several studies will be conducted to obtain a useful scale for human acceptance.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1410: Hybrid Societies: Humans Interacting with Embodied Technologies
• Applicant Institution: Technische Universität Chemnitz
• Project Head: Professorin Dr.-Ing. Ulrike Thomas