Constructive incremental learning from only local information
Neural Computation
Path planning using a lazy spatial network PRM
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
Manipulation planning with workspace goal regions
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Imitation learning with generalized task descriptions
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Reactive grasping using optical proximity sensors
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Humanoid motion planning for dual-arm manipulation and re-grasping tasks
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Estimating the non-linear dynamics of free-flying objects
Robotics and Autonomous Systems
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Performing manipulation tasks interactively in real environments requires a high degree of accuracy and stability. At the same time, when one cannot assume a fully deterministic and static environment, one must endow the robot with the ability to react rapidly to sudden changes in the environment. These considerations make the task of reach and grasp difficult to deal with. We follow a Programming by Demonstration (PbD) approach to the problem and take inspiration from the way humans adapt their reach and grasp motions when perturbed. This is in sharp contrast to previous work in PbD that uses unperturbed motions for training the system and then applies perturbation solely during the testing phase. In this work, we record the kinematics of arm and fingers of human subjects during unperturbed and perturbed reach and grasp motions. In the perturbed demonstrations, the target's location is changed suddenly after the onset of the motion. Data show a strong coupling between the hand transport and finger motions. We hypothesize that this coupling enables the subject to seamlessly and rapidly adapt the finger motion in coordination with the hand posture. To endow our robot with this competence, we develop a coupled dynamical system based controller, whereby two dynamical systems driving the hand and finger motions are coupled. This offers a compact encoding for reach-to-grasp motions that ensures fast adaptation with zero latency for re-planning. We show in simulation and on the real iCub robot that this coupling ensures smooth and ''human-like'' motions. We demonstrate the performance of our model under spatial, temporal and grasp type perturbations which show that reaching the target with coordinated hand-arm motion is necessary for the success of the task.