Review of the techniques used in motor‐cognitive human‐robot skill transfer

Abstract A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of general‐purpose robot skills is mainly reflected in inability in novel and complex scenarios. Skill transfer aims to transfer human skills to general‐purpose manipulators or mobile robots to replicate human‐like behaviours. Skill transfer methods that are commonly used at present, such as learning from demonstrated (LfD) or imitation learning, endow the robot with the expert's low‐level motor and high‐level decision‐making ability, so that skills can be reproduced and generalised according to perceived context. The improvement of robot cognition usually relates to an improvement in the autonomous high‐level decision‐making ability. Based on the idea of establishing a generic or specialised robot skill library, robots are expected to autonomously reason about the needs for using skills and plan compound movements according to sensory input. In recent years, in this area, many successful studies have demonstrated their effectiveness. Herein, a detailed review is provided on the transferring techniques of skills, applications, advancements, and limitations, especially in the LfD. Future research directions are also suggested

Dynamic Sensor Selection for Robotic Systems

A new technique for selecting, in real time, different sensing techniques for a robotic system has been developed. The proposed method is based on stochastic dynamic programming, which provides an effective solution to multi-stage decision problems. At each stage in the decision process a sensor selection controller has the option of consulting a new process monitoring technique to improve the knowledge of the task or terminating the decision process without any further information gathering. The sensor selection controller has been successfully implemented for the real-time control of a planar robotic assembly task in a discrete event control framework. One of the monitoring methods used is based on Hidden Markov Models, where the average recognition rate was 87%. Larger recognition rates for the HMM method have been demonstrated by [4]. The rate of 87% was chosen to show the effectiveness of the dynamic sensor selection method. The experiments show that the method performs better tha..

Frequency-Domain Force Measurements for Discrete Event Contact Recognition

Discrete event recognition based on force measurements in the frequency-domain is presented. The force signals arise from interaction between the workpiece and the environment in a planar assembly task. The discrete events are modeled as Hidden Markov Models (HMMs), where the models are trained off-line with the Baum-Welch re-estimation algorithm. After the HMMs have been trained, we use them on-line in a robotic system to recognise discrete events as they occur. Event recognition with an accuracy as high as 98% was accomplished in 0.5-0.6s with a relatively small training set. 1 Introduction Process plants must deal with changing states, multiple faults, unexpected situations and unreliable measurements. To handle these problems real-time process monitoring is essential. Process monitoring is widely used as a component in many industrial processes. In applications such as robotic assembly, however, there is an increasing need for efficient process monitoring methods to account for u..

Sensitivity Analysis of a Sensory Perception Controller

We have developed a method for the sensitivity analysis of a sensory perception controller (SPC). The SPC performs dynamic process monitor selection in a real-time discrete event control system. The SPC uses stochastic dynamic programming (SDP) to solve the underlying Markov decision process. The SDP approach has been mapped to a linear programming (LP) formulation and the sensitivity analysis is performed using well-known LP techniques. In particular, the sensitivity of the discount factor of future rewards is studied. We show that the SPC has a relatively low sensitivity to variations in this model parameter. 1 Introduction The control of sensory perception allows for an efficient use of available sensors. In nominal operation, only a few sensing techniques (monitors) are required. Then as an anomaly develops, additional sensing techniques are utilised. The average sensing costs for a system using perception control are lower than for a system where all sensors are used all the time..

Nonlinear estimation methods for parameter tracking in power plants

This paper presents parameter estimation of physical time-varying parameters for combined-cycle power plant models. Four different variants of the Kalman filter are compared extensively on models of different complexities. The first filter is the well-known extended Kalman filter. The second and third filters are recent (2000 and 2001) developments of unscented versions of the Kalman filter. The fourth filter is a new (2000) and compact version of the adaptive sensitivity-based Kalman filter. The four different approaches have different complexities, behaviour and advantages that are surveyed in this paper