Learning to predict phases of manipulation tasks as hidden states

Phase transitions in manipulation tasks often occur when contacts between objects are made or broken. A switch of the phase can result in the robot’s actions suddenly influencing different aspects of its environment. Therefore, the boundaries between phases often correspond to constraints or subgoals of the manipulation task. In this paper, we investigate how the phases of manipulation tasks can be learned from data. The task is modeled as an autoregressive hidden Markov model, wherein the hidden phase transitions depend on the observed states. The model is learned from data using the expectation-maximization algorithm. We demonstrate the proposed method on both a pushing task and a pepper mill turning task. The proposed approach was compared to a standard autoregressive hidden Markov model. The experiments show that the learned models can accurately predict the transitions in phases during the manipulation tasks

Methods and Tools for Objective Assessment of Psychomotor Skills in Laparoscopic Surgery

Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentor–trainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons’ psychomotor skills. Ongoing development of tracking systems and software solutions has allowed for the expansion of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentals of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as well as optimize their already overloaded training schedules

Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models

Robot introspection, as opposed to anomaly detection typical in process monitoring, helps a robot understand what it is doing at all times. A robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of sub-tasks. As robots continue their quest of functioning in unstructured environments, it is imperative they understand what is it that they are actually doing to render them more robust. This work investigates the modeling ability of Bayesian nonparametric techniques on Markov Switching Process to learn complex dynamics typical in robot contact tasks. We study whether the Markov switching process, together with Bayesian priors can outperform the modeling ability of its counterparts: an HMM with Bayesian priors and without. The work was tested in a snap assembly task characterized by high elastic forces. The task consists of an insertion subtask with very complex dynamics. Our approach showed a stronger ability to generalize and was able to better model the subtask with complex dynamics in a computationally efficient way. The modeling technique is also used to learn a growing library of robot skills, one that when integrated with low-level control allows for robot online decision making.Comment: final version submitted to humanoids 201

Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies

Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool collisions. Robots must develop the capacity to respond to unexpected events. That is not only identifying the sudden anomaly, but also deciding how to handle it. In this work, we contribute a recovery policy that allows a robot to recovery from various anomalous scenarios across different tasks and conditions in a consistent and robust fashion. The system organizes tasks as a sequence of nodes composed of internal modules such as motion generation and introspection. When an introspection module flags an anomaly, the recovery strategy is triggered and reverts the task execution by selecting a target node as a function of a state dependency chart. The new skill allows the robot to overcome the effects of the external disturbance and conclude the task. Our system recovers from accidental human and tool collisions in a number of tasks. Of particular importance is the fact that we test the robustness of the recovery system by triggering anomalies at each node in the task graph showing robust recovery everywhere in the task. We also trigger multiple and repeated anomalies at each of the nodes of the task showing that the recovery system can consistently recover anywhere in the presence of strong and pervasive anomalous conditions. Robust recovery systems will be key enablers for long-term autonomy in robot systems. Supplemental info including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl

Estimation of Phases for Compliant Motion

Nowadays adding a skill to the robot that can interact with the environment is the primary goal of many researchers. The intelligence of the robot can be achieved by segmenting the manipulation task into phases which are subgoals of the task and identifying the transition between them. This thesis proposes an approach for predicting the number of phases of a compliant motion based manipulation task and estimating their corresponding HMM model that best fit with each segmented phase of the task. Also, it addresses the problem of phase transition monitoring by using recorded data. The captured data is utilized for the building an HMM model, and in the framework of task segmentation, the phase transition addressed. In this thesis, the concept of non-homogeneous HMM is used in modeling the manipulation task, wherein hidden phase depends on observed effect of performing an action (force). The expectation-maximization (EM) algorithm employed in estimating the parameters of the HMM model. The EM algorithm guarantees the estimation of the optimal parameters for each phase of the manipulation task. Hence the modeling accuracy of the forced based transition is significantly enhanced compared to position based transition. To see the performance of the phase transition detection a Viterbi algorithm was implemented. A Cartesian impedance controller defined by [6] for each phase detected is used to reproduce the learned task. The proposed approach is investigated with a KUKA LWR4+ arm in two test setups: in the first, we use parameter estimation for a single demonstration with three phases, and in the second experiment, we find a generalization of the parameter estimation for multiple demonstrations. For both experiments, the transition between phases of the manipulation task is identified. We conclude that our method provides a convenient platform for modeling and estimating of model parameters for phases of manipulation task from single and double demonstrations

Modelling individual variability in cognitive development

Investigating variability in reasoning tasks can provide insights into key issues in the study of cognitive development. These include the mechanisms that underlie developmental transitions, and the distinction between individual differences and developmental disorders. We explored the mechanistic basis of variability in two connectionist models of cognitive development, a model of the Piagetian balance scale task (McClelland, 1989) and a model of the Piagetian conservation task (Shultz, 1998). For the balance scale task, we began with a simple feed-forward connectionist model and training patterns based on McClelland (1989). We investigated computational parameters, problem encodings, and training environments that contributed to variability in development, both across groups and within individuals. We report on the parameters that affect the complexity of reasoning and the nature of ‘rule’ transitions exhibited by networks learning to reason about balance scale problems. For the conservation task, we took the task structure and problem encoding of Shultz (1998) as our base model. We examined the computational parameters, problem encodings, and training environments that contributed to variability in development, in particular examining the parameters that affected the emergence of abstraction. We relate the findings to existing cognitive theories on the causes of individual differences in development