Imitation learning has been studied widely due to its convenient transfer of human experiences to robots. This learning approach models human demonstrations by extracting relevant motion patterns as well as adaptation to different situations. In order to address unpredicted situations such as obstacles and external perturbations, motor skills adaptation is crucial and non-trivial, particularly in dynamic or unstructured environments. In this paper, we propose to tackle this problem using a novel kernelized movement primitive (KMP) adaptation, which not only allows the robot to adapt its motor skills and meet a variety of additional task constraints arising over the course of the task, but also renders fewer open parameters unlike methods built on basis functions. Moreover, we extend our approach by introducing the concept of local frames, which represent coordinate systems of interest for tasks and could be modulated in accordance with external task parameters, endowing KMP with reliable extrapolation abilities in a broader domain. Several examples of trajectory modulations and extrapolations verify the effectiveness of our method.
Similarly to our approach, information theory has also been exploited in different robot learning techniques. As an effective way to measure the distance between two probabilistic distributions, KL-divergence was exploited in policy search , trajectory optimization and imitation learning . In @cite_5 KL-divergence was used to measure the difference between the distributions of demonstrations and the predicted robot trajectories (obtained from a control policy and a Gaussian process forward model), and subsequently the probabilistic inference for learning control was employed to iteratively minimize the KL-divergence so as to find the optimal policy parameters. It is noted that this KL-divergence formulation makes the derivations of analytical solution intractable. In this article, we formulate the trajectory matching problem as ), which allows us to separate the mean and covariance subproblems and derive closed-form solutions for them separately.
@cite_5
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Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot's abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bi-manual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot. These results provide a good starting point for more complex and interactive learning tasks.