Visual features can help predict if a manipulation behavior will succeed at a given location. For example, the success of a behavior that flips light switches depends on the location of the switch. Within this paper, we present methods that enable a mobile manipulator to autonomously learn a function that takes an RGB image and a registered 3D point cloud as input and returns a 3D location at which a manipulation behavior is likely to succeed. Given a pair of manipulation behaviors that can change the state of the world between two sets (e.g., light switch up and light switch down), classifiers that detect when each behavior has been successful, and an initial hint as to where one of the behaviors will be successful, the robot autonomously trains a pair of support vector machine (SVM) classifiers by trying out the behaviors at locations in the world and observing the results. When an image feature vector associated with a 3D location is provided as input to one of the SVMs, the SVM predicts if the associated manipulation behavior will be successful at the 3D location. To evaluate our approach, we performed experiments with a PR2 robot from Willow Garage in a simulated home using behaviors that flip a light switch, push a rocker-type light switch, and operate a drawer. By using active learning, the robot efficiently learned SVMs that enabled it to consistently succeed at these tasks. After training, the robot also continued to learn in order to adapt in the event of failure.
There is a significant body of work on robots learning to manipulate autonomously, robots learning to perceive, perception for manipulation that exploits task structure, and active learning methods for using labeling efforts efficiently. In this section, we discuss the relationship between our work and current learning methods as well as work that has demonstrated the effectiveness of using task-relevant cues for perception in human environments. The research we present in this paper builds on our earlier workshop publication @cite_63 .
@cite_63
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A major bottleneck in the realization of autonomous robotic agents performing complex manipulation tasks are the requirements that these tasks impose onto perception mechanisms. There is a strong need to scale robot perception capabilities along two dimensions: First, the variations of appearances and perceptual properties that real-world objects exhibit. Second, the variety of perceptual tasks, like categorizing and localizing, decomposing objects into their functional parts, perceiving the affordances they provide. This paper, addresses this need by organizing perception into a two-stage process. First, a pervasive and 'calm' perceptual component runs continually and interprets the incoming image stream to form a general purpose hybrid (symbolic sub-symbolic) belief state. This is used by the second component, the task-directed perception subsystem, to perform the respective perception tasks in a more informed way. We describe and discuss the first component and explain how it can manage realistic belief states, form a memory of past perceptual experiences, and compute valuable perceptual attributes without delaying plan execution. It does so by exploiting that perception is not a one-shot task but rather a secondary task that is pervasively and calmly performed throughout the lifetime of the robot. We show system operating on a leading-edge manipulation platform.