Generalizing Learned Manipulation Skills in Practice

Robots should be able to learn and perform a manipulation task across different settings. This paper presents an approach that learns an RNN-based manipulation skill model from demonstrations and then generalizes the learned skill in new settings. The manipulation skill model learned from demonstrations in an initial set of setting performs well in those settings and similar ones. However, the model may perform poorly in a novel setting that is significantly different from the learned settings. Therefore a novel approach called generalization in practice (GiP) is developed to tackle this critical problem. In this approach, the robot practices in the new setting to obtain new training data and refine the learned skill using the new data to gradually improve the learned skill model. The proposed approach has been implemented for one type of manipulation task – pouring that is the most performed manipulation in cooking applications. The presented approach enables a pouring robot to pour gracefully like a person in terms of speed and accuracy in learned setups and gradually improve the pouring performance in novel setups after several practices.

Authors

• Juan Wilches
• Yongqiang Huang 0001
• Yu Sun 0004

Keywords

• Training
• Solid modeling
• Volume measurement
• Measurement uncertainty
• Training data
• Containers
• Task analysis
• Learning Models
• Learning Skills
• Manipulation Tasks
• Error Of The Mean
• Time Step
• Outcome Data
• Previous Step
• Set Of Functions
• Actual Results
• Long Short-term Memory
• Recurrent Neural Network
• Angular Velocity
• Constant Velocity
• Previous Time
• Standard Deviation Error
• Simple Control
• Force Sensor
• Recurrent Neural Network Model
• Bottle Of Wine
• Forward Velocity
• Soft Objects
• Robot Learning
• Use Of Motion
• Neural Network
• Volume Of Water
• Long Short-term Memory Model
• Long Short-term Memory Unit
• Training Dataset