Learning task-relevant parameters for human and robot motion primitives to acquire complex manipulation skills on a humanoid robot

• Applicant: Dr. Freek Stulp
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2009 to 2012
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 128859953

Although robots are currently faster, stronger, and more accurate than humans, they are still far from achieving human-like performance when manipulating objects. The main reason is that the brain as a motion controller is far superior over robotic controllers in terms of flexibility, autonomous learning abilities, and reliability. In the long run, such characteristics are necessary requirements for robots too; as, for instance, needed in elderly care or physical therapy, but also for the design of effective robotic prostheses. An important strategy in achieving higher learning abilities and more flexibility in generating a large movement repertoire has focused on the idea of motion primitives. Motion primitives are short, goal-directed and task-specific movements of reduced complexity. They can be sequenced or superimposed in order to achieve more complex movement skills. Unfortunately, parameter spaces for robot motion primitives are often not directly related to task success, i.e., the parameters are in a high-dimensional abstract space with a complex relationships to actual task goal. Therefore, the first aim of this project is to learn task-relevant parameters for motion primitives using dimensionality reduction techniques. Examples of such parameters are “at which height to grasp a glass” or “at which position to reach for a faucet”. We focus on learning task-relevant object manipulation for humanoid robots. Given the similarity between current humanoid robots and humans, we intend to prime robot exploration with the task-relevant parameter space extracted from human data. Having low-dimensional abstract motion primitives parameters simplifies motion planning for the robot. The second main goal of the project is therefore to enable the robot to optimize whole sequences of motion primitives with reinforcement learning methods, particularly ideas from policy gradients and probabilistic reinforcement learning. Optimized motion primitive sequences will provide robots with a more complete and refined library of complex motor skills.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Dr.-Ing. Stefan Schaal