Grasp Recognition for Programming by Demonstration

The demand for flexible and re-programmable robots has increased the need for programming by demonstration systems. In this paper, grasp recognition is considered in a programming by demonstration framework. Three methods for grasp recognition are presented and evaluated. The first method uses Hidden Markov Models to model the hand posture sequence during the grasp sequence, while the second method relies on the hand trajectory and hand rotation. The third method is a hybrid method, in which both the first two methods are active in parallel. The particular contribution is that all methods rely on the grasp sequence and not just the final posture of the hand. This facilitates grasp recognition before the grasp is completed. Also, by analyzing the entire sequence and not just the final grasp, the decision is based on more information and increased robustness of the overall system is achieved. The experimental results show that both arm trajectory and final hand posture provide important information for grasp classification. By combining them, the recognition rate of the overall system is increased.

Authors

• Staffan Ekvall
• Danica Kragic

Keywords

• Robot programming
• Humans
• Hidden Markov models
• Robustness
• Data mining
• Virtual environment
• Robotics and automation
• Computer vision
• Robot vision systems
• Information analysis
• Hidden Markov Model
• Hybrid Method
• Recognition Rate
• Hand Position
• Training Data
• Prosthesis
• Experimental Evaluation
• Visual Feedback
• Multiple Users
• Hybrid System
• Sensor Locations
• Optimal Weight
• Training Examples
• Discrete Fourier Transform
• Measure Of Confidence
• Dissimilarity Measure
• Euler Angles
• Usage Intention
• Position Trajectory
• Reference Sensor
• Hand Shape
• Sensor Orientation
• Shift In Space