We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the motion flux', a quantity which captures the motion variation of a group of skelet al joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis.
In robotics the paradigm of transferring human motion primitives to robot movements is paramount for imitation learning and, more recently to implement human-robot collaboration @cite_1 . A good amount of research in robotics has approached primitives in terms of Dynamic Movement Primitives (DMP) @cite_1 to model elementary motor behaviors as attractor systems, representing them with differential equations. Typical applications are learning by imitation or learning from demonstration @cite_53 @cite_58 @cite_71 @cite_42 , learning task specifications @cite_27 , modeling interaction primitives @cite_18 . Motion primitives are represented either via Hidden Markov models or Gaussian Mixture Models (GMM). @cite_30 present an approach based on HMM for imitation learning of arm movements, and @cite_3 model arm motion primitives via GMM.
@cite_30 @cite_18 @cite_53 @cite_42 @cite_1 @cite_3 @cite_27 @cite_71 @cite_58
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Dynamic system-based motor primitives have enabled robots to learn complex tasks ranging from Tennis-swings to locomotion. However, to date there have been only few extensions which have incorporated perceptual coupling to variables of external focus, and, furthermore, these modifications have relied upon handcrafted solutions. Humans learn how to couple their movement primitives with external variables. Clearly, such a solution is needed in robotics. In this paper, we propose an augmented version of the dynamic systems motor primitives which incorporates perceptual coupling to an external variable. The resulting perceptually driven motor primitives include the previous primitives as a special case and can inherit some of their interesting properties. We show that these motor primitives can perform complex tasks such a Ball-in-a-Cup or Kendama task even with large variances in the initial conditions where a skilled human player would be challenged. For doing so, we initialize the motor primitives in the traditional way by imitation learning without perceptual coupling. Subsequently, we improve the motor primitives using a novel reinforcement learning method which is particularly well-suited for motor primitives. We describe a new method for representing human movement compactly, in terms of a linear super-imposition of simpler movements termed i>primitives. This method is a part of a larger research project aimed at modeling motor control and imitation using the notion of perceptuo-motor primitives, a basis set of coupled perceptual and motor routines. In our model, the perceptual system is biased by the set of motor behaviors the agent can execute. Thus, an agent can automatically classify observed movements into its executable repertoire. In this paper, we describe a method for automatically deriving a set of primitives directly from human movement data. We used movement data gathered from a psychophysical experiment on human imitation to derive the primitives. The data were first filtered, then segmented, and principal component analysis was applied to the segments. The eigenvectors corresponding to a few of the highest eigenvalues provide us with a basis set of primitives. These are used, through superposition and sequencing, to reconstruct the training movements as well as novel ones. The validation of the method was performed on a humanoid simulation with physical dynamics. The effectiveness of the motion reconstruction was measured through an error metric. We also explored and evaluated a technique of clustering in the space of primitives for generating controllers for executing frequently used movements. We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method allows for iterative execution and motion refinement using a forgetting factor. During the iterative motion refinement, a confidence value specifies an area of allowed refinement around the nominal trajectory. A novel method for continuous generation of motions from a hidden Markov model (HMM) representation of motion primitives is proposed, which incorporates relative time information for each state. On the real-time control level, the kinesthetic teaching is handled by a customized impedance controller, which combines tracking performance with soft physical interaction and allows to implement soft boundaries for the motion refinement. The proposed methods were implemented and tested using DLR's humanoid upper-body robot Justin. Dynamic Movement Primitives (DMPs) are a common method for learning a control policy for a task from demonstration. This control policy consists of differential equations that can create a smooth trajectory to a new goal point. However, DMPs only have a limited ability to generalize the demonstration to new environments and solve problems such as obstacle avoidance. Moreover, standard DMP learning does not cope with the noise inherent to human demonstrations. Here, we propose an approach for robot learning from demonstration that can generalize noisy task demonstrations to a new goal point and to an environment with obstacles. This strategy for robot learning from demonstration results in a control policy that incorporates different types of learning from demonstration, which correspond to different types of observational learning as outlined in developmental psychology.