Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

In this chapter, we mainly introduce the definition, background, significance, and the start-of-the-art methods of collaborative robot multimodal introspection. The current issues of robot introspection are also introduced, which including the complex task representation, anomaly monitoring, diagnoses and recovery by assessing the quality of multimodal sensory data during robot manipulation. The overall content of this book is presented at the end.

In this chapter, we take a Bayesian nonparametric approach in defining a prior on the hidden Markov model that allows for flexibility in addressing the problem of modeling the complex dynamics during robot manipulation task. At first, considering the underlying dynamics that can be well-modeled as a hidden discrete Markov process, but in which there is uncertainty about the cardinality of the state space. Through the use of the hierarchical Dirichlet process (HDP), one can examine an HMM with an unbounded number of possible states. Subsequently, the sticky HDP-HMM is investigated for allowing more robust learning of the complex dynamics through a learned bias by increasing the probability of self-transitions. Additionally, although the HDP-HMM and its sticky extension are very flexible time series models, they make a strong Markovian assumption that observations are conditionally independent given the discrete HMM state. This assumption is often insufficient for capturing the temporal dependencies of the observations in real data. To address this issue, we consider extensions of the sticky HDP-HMM for learning the switching dynamical processes with switching linear dynamical system. In the later chapters of this book, we will verify the performances in modeling mulitmodal time series and present the results of robot movement identification, anomaly monitoring, and anomaly diagnose.

In this chapter, we present a novel method for incremental learning robot complex task representation, identifying repeated skills, and generalizing to new environment by heuristically segmenting the unstructured demonstrations into movement primitives that modelled with a dynamical system. The proposed method combines the advantages of recent task representation methods for learning from demonstration in into an integrated framework. In particular, we use the combination of finite state machine and dynamical movement primitives for complex task representation, and investigate the Bayesian nonparametric hidden Markov model for repeated skill identification. To this end, a robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of skills, which helps a robot understand what it is doing at all times. Two complex, multi-step robot tasks are designed to evaluate the feasibility and effectiveness of proposed methods. We not only present the results in task representation, but also analyzing the performance of skill identification by various nonparametric models with various modality combinations.

In this chapter, we introduce an anomaly monitoring pipeline using the Bayesian nonparametric hidden Markov models after the task representation and skill identification in previous chapter, which divided into three categories according to different thresholds definition, including (i) log-likelihood-based threshold, (ii) threshold based on the gradient of log-likelihood, and (iii) computing the threshold by mapping latent state to log-likelihood. Those method are effectively implement the anomaly monitoring during robot manipulation task. We also evaluate and analyse the performance and results for each method, respectively.

In this chapter, we introduce two novel anomaly diagnose methods using the Bayesian nonparametric hidden Markov models when anomaly triggered, including i)multi-class classifier based on nonparametric models, ii) sparse representation by statistical feature extraction for anomaly diagnose. Additionally, the detail procedure for anomaly sample definition, the supervised learning dataset collection as well as the data augmentation of insufficient samples are also declared. We evaluated the proposed methods with a multi-step human-robot collaboration objects kitting task on Baxter robot, the performance and results are presented of each method respectively.

In this chapter, the anomaly recovery would be acted when both of the anomaly monitoring and diagnoses are analysed, which aim to respond the external disturbances from the environmental changes or human intervention in the increasingly human-robot scenarios. To effectively evaluate the exploration, we summarize the anomalies in a robot system include only two catalogues: accidental anomalies and persistent anomalies. In particular, we first diagnose the anomaly as accidental one at the beginning such that the reverse execution is called. If and only if robot reverse many times (not less than twice) and still couldn’t avoid or eliminate the anomaly, the human interaction is called. Our proposed system would synchronously record the multimodal sensory signals during the process of human-assisted demonstration. That is, a new movement primitive is learned once an exploring demonstration acquired. Then, we heuristically generate a set of synthetic demonstrations for augmenting the learning by appending a multivariate Gaussian noise distribution with mean equal to zeros and covariance equal to ones. Such that the corresponding introspective capabilities are learned and updated when another human demonstration is acquired. Consequently, incrementally learning the introspective movement primitives with few human corrective demonstrations when an unseen anomaly occurs. It is essential that, although there are only two different exploring strategies when anomaly occurs, numerous exploring behaviors can be generated according to different anomaly types and movement behaviors under various circumstances.