Autonomously Learning to Visually Detect Where Manipulation Will Succeed

摘要: Visual features can help predict if a manipulation behavior will succeed at given location. For example, the success of that flips light switches depends on location switch. Within this paper, we present methods enable mobile manipulator to autonomously learn function takes an RGB image and registered 3D point cloud as input returns which is likely succeed. Given pair behaviors change state world between two sets (e.g., switch up down), classifiers detect when each has been successful, initial hint where one be robot trains support vector machine (SVM) by trying out locations in observing results. When feature associated with provided SVMs, SVM predicts successful To evaluate our approach, performed experiments PR2 from Willow Garage simulated home using flip switch, push rocker-type operate drawer. By active learning, efficiently learned SVMs enabled it consistently these tasks. After training, also continued order adapt event failure.