Human-Like Grasping With Robot-Like Reactiveness
Humans handle manipulation under uncertainty by touching objects to guide their motion and they use the feel of touch to react to any deviation. The key is to leverage the environment to reduce perception and motion uncertainty and to generate distinguishable feedback signals.
This is difficult for robots because representing the environment and dynamic interactions are either a weak approximation or too complex to plan robustly or efficiently. But we can design human-like manipulation strategies for robots and some motion planners can generate reactive robot behaviors, where the robot can take alternative actions based on senor event during execution. The challenge is to extend human-like manipulation strategies with robot-like reactiveness.
Description of Work
The student will design alternative manipulation strategies for daily activities (e.g., grasping and opening any door handle) which strategies than will be improved with reactive behavior by using contact- and sampling-based motion planning. The student will evaluate both human-like and improved strategies on our WAM or Panda 7DOF robots.
For this project you need good C++ programming skills. Experience with the robotics-library (from our robotics and compbio classes) or is a plus.
Előd Páll 
Oliver Brock