Model identification and model analysis in robot training

Robot training is a fast and efficient method of obtaining robot control code. Many current machine learning paradigms used for this purpose, however, result in opaque models that are difficult, if not impossible to analyse, which is an impediment in safety-critical applications or application scenarios where humans and robots occupy the same workspace. In experiments with a Magellan Pro mobile robot we demonstrate that it is possible to obtain transparent models of sensor-motor couplings that are amenable to subsequent analysis, and how such analysis can be used to refine and tune the models post hoc

An application of lyapunov stability analysis to improve the performance of NARMAX models

Previously we presented a novel approach to program a robot controller based on system identification and robot training techniques. The proposed method works in two stages: first, the programmer demonstrates the desired behaviour to the robot by driving it manually in the target environment. During this run, the sensory perception and the desired velocity commands of the robot are logged. Having thus obtained training data we model the relationship between sensory readings and the motor commands of the robot using ARMAX/NARMAX models and system identification techniques. These produce linear or non-linear polynomials which can be formally analysed, as well as used in place of “traditional robot” control code. In this paper we focus our attention on how the mathematical analysis of NARMAX models can be used to understand the robot’s control actions, to formulate hypotheses and to improve the robot’s behaviour. One main objective behind this approach is to avoid trial-and-error refinement of robot code. Instead, we seek to obtain a reliable design process, where program design decisions are based on the mathematical analysis of the model describing how the robot interacts with its environment to achieve the desired behaviour. We demonstrate this procedure through the analysis of a particular task in mobile robotics: door traversal

Towards modeling complex robot training tasks through system identification

Previous research has shown that sensor-motor tasks in mobile robotics applications can be modelled automatically, using NARMAX system identi�cation, where the sensory perception of the robot is mapped to the desired motor commands using non-linear polynomial functions, resulting in a tight coupling between sensing and acting | the robot responds directly to the sensor stimuli without having internal states or memory. However, competences such as for instance sequences of actions, where actions depend on each other, require memory and thus a representation of state. In these cases a simple direct link between sensory perception and the motor commands may not be enough to accomplish the desired tasks. The contribution to knowledge of this paper is to show how fundamental, simple NARMAX models of behaviour can be used in a bootstrapping process to generate complex behaviours that were so far beyond reach. We argue that as the complexity of the task increases, it is important to estimate the current state of the robot and integrate this information into the system identification process. To achieve this we propose a novel method which relates distinctive locations in the environment to the state of the robot, using an unsupervised clustering algorithm. Once we estimate the current state of the robot accurately, we combine the state information with the perception of the robot through a bootstrapping method to generate more complex robot tasks: We obtain a polynomial model which models the complex task as a function of predefined low level sensor motor controllers and raw sensory data. The proposed method has been used to teach Scitos G5 mobile robots a number of complex tasks, such as advanced obstacle avoidance, or complex route learning

Robot training using system identification

This paper focuses on developing a formal, theory-based design methodology to generate transparent robot control programs using mathematical functions. The research finds its theoretical roots in robot training and system identification techniques such as Armax (Auto-Regressive Moving Average models with eXogenous inputs) and Narmax (Non-linear Armax). These techniques produce linear and non-linear polynomial functions that model the relationship between a robot’s sensor perception and motor response. The main benefits of the proposed design methodology, compared to the traditional robot programming techniques are: (i) It is a fast and efficient way of generating robot control code, (ii) The generated robot control programs are transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour, and (iii) It requires very little explicit knowledge of robot programming where end-users/programmers who do not have any specialised robot programming skills can nevertheless generate task-achieving sensor-motor couplings. The nature of this research is concerned with obtaining sensor-motor couplings, be it through human demonstration via the robot, direct human demonstration, or other means. The viability of our methodology has been demonstrated by teaching various mobile robots different sensor-motor tasks such as wall following, corridor passing, door traversal and route learning

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

Complex robot training tasks through bootstrapping system identification

Many sensor-motor competences in mobile robotics applications exhibit complex, non-linear characteristics. Previous research has shown that polynomial NARMAX models can learn such complex tasks. However as the complexity of the task under investigation increases, representing the whole relationship in one single model using only raw sensory inputs would lead to large models. Training such models is extremely difficult, and, furthermore, obtained models often exhibit poor performances. This paper presents a bootsrapping method of generating complex robot training tasks using simple NARMAX models. We model the desired task by combining predefined low level sensor motor controllers. The viability of the proposed method is demonstrated by teaching a Scitos G5 autonomous robot to achieve complex route learning tasks in the real world robotics experiments

Comparing robot controllers through system identification

In the mobile robotics field, it is very common to find different control programs designed to achieve a particular robot task. Although there are many ways to evaluate these controllers qualitatively, there is a lack of formal methodology to compare them from a mathematical point of view. In this paper we present a novel approach to compare robot control codes quantitatively based on system identification: Initially the transparent mathematical models of the controllers are obtained using the NARMAX system identification process. Then we use these models to analyse the general characteristics of the cotrollers from a mathematical point of view. In this way, we are able to compare different control programs objectively based on quantitative measures. We demonstrate our approach by comparing two different robot control programs, which were designed to drive the robot through door-like openings

Route training in mobile robots through system identification

Fundamental sensor-motor couplings form the backbone of most mobile robot control tasks, and often need to be implemented fast, efficiently and nevertheless reliably. Machine learning techniques are therefore often used to obtain the desired sensor-motor competences. In this paper we present an alternative to established machine learning methods such as artificial neural networks, that is very fast, easy to implement, and has the distinct advantage that it generates transparent, analysable sensor-motor couplings: system identification through nonlinear polynomial mapping. This work, which is part of the RobotMODIC project at the universities of Essex and Sheffield, aims to develop a theoretical understanding of the interaction between the robot and its environment. One of the purposes of this research is to enable the principled design of robot control programs. As a first step towards this aim we model the behaviour of the robot, as this emerges from its interaction with the environment, with the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving Average models with eXogenous inputs). This method produces explicit polynomial functions that can be subsequently analysed using established mathematical methods. In this paper we demonstrate the fidelity of the obtained NARMAX models in the challenging task of robot route learning; we present a set of experiments in which a Magellan Pro mobile robot was taught to follow four different routes, always using the same mechanism to obtain the required control law

Learning by observation through system identification

In our previous works, we present a new method to program mobile robots —“code identification by demonstration”— based on algorithmically transferring human behaviours to robot control code using transparent mathematical functions. Our approach has three stages: i) first extracting the trajectory of the desired behaviour by observing the human, ii) making the robot follow the human trajectory blindly to log the robot’s own perception perceived along that trajectory, and finally iii) linking the robot’s perception to the desired behaviour to obtain a generalised, sensor-based model. So far we used an external, camera based motion tracking system to log the trajectory of the human demonstrator during his initial demonstration of the desired motion. Because such tracking systems are complicated to set up and expensive, we propose an alternative method to obtain trajectory information, using the robot’s own sensor perception. In this method, we train a mathematical polynomial using the NARMAX system identification methodology which maps the position of the “red jacket” worn by the demonstrator in the image captured by the robot’s camera, to the relative position of the demonstrator in the real world according to the robot. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve door traversal behaviour