Robots can generalize manipulation skills between different scenarios by adapting to the features of the objects being manipulated. Selecting the set of relevant features for generalizing skills has usually been performed manually by a human. Alternatively, a robot can learn to select relevant features autonomously. However, feature selection usually requires a large amount of training data, which would require many demonstrations. In order to learn the relevant features more efficiently, we propose using a meta-level prior to transfer the relevance of features from previously learned skills. The experiments show that the meta-level prior more than doubles the average precision and recall of the feature selection when compared to a standard uniform prior. The proposed approach was used to learn a variety of manipulation skills, including pushing, cutting, and pouring.
@cite_5: The state of the art in and the future of robotics are discussed. The potential paths to the long-term vision of robots that work alongside people in homes and workplaces as useful, capable collaborators are discussed. Robot manipulation in human environments is expected to grow in the coming years as more researchers seek to create robots that actively help in the daily lives of people
@cite_47: In a Robot Learning from Demonstration framework involving environments with many objects, one of the key problems is to decide which objects are relevant to a given task. In this paper, we analyze this problem and propose a biologically-inspired computational model that enables the robot to focus on the task-relevant objects. To filter out incompatible task models, we compute a Task Relevance Value (TRV) for each object, which shows a human demonstrator's implicit indication of the relevance to the task. By combining an intentional action representation with ‘motionese’ [2], our model exhibits recognition capabilities compatible with the way that humans demonstrate. We evaluate the system on demonstrations from five different human subjects, showing its ability to correctly focus on the appropriate objects in these demonstrations.
Another important challenge for skill learning is determining which objects are involved in a manipulation @cite_5 . The set of relevant objects can often be extracted from demonstrations using visual cues, such as motionese @cite_47 . The object selection problem is however distinct from the feature selection problem addressed in this paper, and not all of the features associated with an object will be relevant for generalizing a skill.