Grasp planning based on strategy extracted from demonstration

In this paper, we discuss information that is beneficial to robotic grasp planning and can be extracted from human demonstration. We present a method that integrates grasp intention: grasp type, and the relative thumb positions and orientations on the grasped object to the force-closure-based grasp planning procedure. Instead of completely mimicking the human grasp, grasp type and the relative thumb position are partially extracted from the demonstration to represent the task properties and grasp strategies, and avoid the challenging kinematic correspondence problem. Instead of mapping the demonstrated motion, the grasp type and thumb position provide meaningful constraints on hand posture and wrist position. Both the feasible workspace of a robotic hand and the search space of grasp planning are thereby highly reduced by the constraints. This approach has been evaluated in a simulation with a Barrett hand and a Shadow hand on eight daily objects.

Authors

• Yun Lin 0003
• Yu Sun 0004

Keywords

• Thumb
• Robots
• Joints
• Planning
• Wrist
• Kinematics
• Optimization
• Grasp Planning
• Kinematic
• Search Space
• Hand Position
• Planning Procedures
• Robotic Hand
• Wrist Position
• Objective Function
• Dimensionality Reduction
• Contact Point
• Position Error
• Joint Angles
• Task Requirements
• Object Parts
• Object Surface
• Contact Region
• Metacarpophalangeal Joints
• Human Hand
• Hand Motion
• Robot Model
• Inverse Reinforcement Learning
• Proximal Interphalangeal
• Robot Workspace
• Finger Joints
• Maximum Value Of Function
• Homogeneous Matrix
• Relative Pitch
• Rotation Axis