Robot Imitation Learning Based On Non-contact Observation

Behavioral imitation learning is one of key technologies for robot application.Imitation learning enables robots to transform demonstrated behaviors to their actions,which is an inevitable and effective method of behavioral imitation learning in robotics.Observation and representation-reproduction are important contents of imitation learning.There exist many behavioral observation methods by contact, which often need complexdevices and advanced professional knowledge and are hard to be applied. The proposedmodels for representation and reproduction can handle different layer and class behaviors.Therefore, in this dissertation the imitation learning of non-contact observation based onvision was researched in depth.The relationship between human and robot for visual observation was built formodeling of imitation learning and research efficiency and security. The real time controlalgorithm of end-effector differential motion was proposed to control its velocity. Imitationlearning system, which integrated3D simulation with real robots, was built to increaseresearch efficiency.For the problem on visual behavioral observation in a general unmarked scene,firstly, two local invariant descriptors were proposed, which were main-sub featuredescriptor (MSKD) and binary intensity discrete sampling (BIDS). The relationshipsbetween main key and sub-keys were used to construct descriptors by MSKD, whichavoided computing irrelevant points and represented the patches of key points effectively.BIDS constructed descriptors by sampling discretely around the keys, which overcome theproblem on lighting effects, and sped up the feature matching. The time costs of computingper key and matching of proposed descriptors were less than traditional methods. Secondly,a feature training algorithm based on sample image affine warping was proposed. In thisalgorithm, the affine transform was used to simulate observed images in different views,and the features in different affine transformed images of the same key in sample imagewere integrated. The MSKD and BIDS trained by the proposed method were superior totraditional methods in matching accuracy. Finally, the real-time objective recognition and positioning based on RGB-D image were built. The foreground segmentation based ondepth image shielded regions of irrelevant background image, which sped up featureextraction. The objective positions of actual space on the camera coordinate werecomputed in real time and accurately based on depth camera model. Experiment testifiesthat the proposed methods can deal with visual behavioral observation in a generalunmarked environment.For the problems on representing and reproducing behaviors in different types andlevels, the cybernetic graph (CGM) model ware proposed, which represented a behaviorwith a graph, in which each node was a behavior primitive that had specific signification.The method of representing behavioral primitives based on B-Spline curve, and thealgorithm of behavioral primitive real-time control based on dynamic programming wereproposed, which represented and reproduced different types of behavioral primitivetrajectories. Simulations and experiments testifies that CGM represents and reproducesbehaviors in different types and levels in different robots.For robot behavior imitation learning from visual observed information, learningmethod of CGM was proposed, which was suitable for visual observed sequences. Firstly,the problems on difference in scale and shaking of sequences were resolved bynormalizing scale and smooth filter. Secondly, the method of sequence segmentation basedon correlation function segmented sequences into sub-sequences for learning. Thirdly, thelearning algorithm for behavioral primitive trajectories based on gradient descent withconstraint of arc length was proposed, which transformed observation sequences intoB-Spline curve. Finally, the parameters of behavioral primitives were functionalized by anew generalization boosting algorithm, which enhanced generalization performances.Simulations testifies that the proposed methods were efficient, and the multi-instancesynthetical experiment of imitation learning from visual observation on RCA87A andYamaha robots approves that the proposed methods in this dissertation are efficient,commonly used and applied.