3 research outputs found
Programming-by-demonstration of reaching motions for robot grasping
Proceedings of: 14th International Conference on Advanced Robotics (ICAR 2009), 22-26 June 2009, Munich (Germany)This paper presents a novel approach to skill
modeling acquired from human demonstration. The approach
is based on fuzzy modeling and is using a planner for generating
corresponding robot trajectories. One of the main challenges
stems from the morphological differences between human and
robot hand/arm structure, which makes direct copying of human
motions impossible in the general case. Thus, the planner
works in hand state space, which is defined such that it is
perception-invariant and valid for both human and robot hand.
We show that this representation simplifies task reconstruction
and preserves the essential parts of the task as well as the
coordination between reaching and grasping motion. We also
show how our approach can generalize observed trajectories
based on multiple demonstrations and that the robot can match
a demonstrated behavoir, despite morphological differences.
To validate our approach we use a general-purpose robot
manipulator equipped with an anthropomorphic three-fingered
robot hand.European Community's Seventh Framework Progra
Learning of Generalized Manipulation Strategies in Service Robotics
This thesis makes a contribution to autonomous robotic manipulation. The core is a novel constraint-based representation of manipulation tasks suitable for flexible online motion planning. Interactive learning from natural human demonstrations is combined with parallelized optimization to enable efficient learning of complex manipulation tasks with limited training data. Prior planning results are encoded automatically into the model to reduce planning time and solve the correspondence problem