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Purpose of Review The review presents an overview of advanced robot programming approaches which aims to ease robot programming and speed up the deployment of industrial robots, and then some considerations are shared with respect to requirements in new trends of manufacturing. Recent Findings The new trend of customization along with Industry 4.0 is appearing which is a challenge for robotized systems. The bottleneck is mainly the efficiency of deployment of industrial robots because traditional programming methods are not intuitive and always time-consuming. Advanced robot programming techniques are expected to ease robot programming and make it accessible for non-experts. Summary A review on advanced robot programming is here presented, firstly introducing the background of this research, followed by reviewing literatures in four categories: programming by demonstration for low-level motion, programming by demonstration for high-level task, speech recognition-based and augmented reality-based programming approaches, and finishing on discussing future works.

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Purpose In robot programming by demonstration (PbD) of small parts assembly tasks, the accuracy of parts poses estimated by vision-based techniques in demonstration stage is far from enough to ensure a successful execution. This paper aims to develop an inference method to improve the accuracy of poses and assembly relations between parts by integrating visual observation with computer-aided design (CAD) model. Design/methodology/approach In this paper, the authors propose a spatial information inference method called probabilistic assembly graph with optional CAD model, shorted as PAGC*, to achieve this task. Then an assembly relation extraction method from CAD model is designed, where different assembly relation descriptions in CAD model are summarized into two fundamental relations that are colinear and coplanar. The relation similarity, distance similarity and rotation similarity are adopted as the similar part matching criterions between the CAD model and the observation. The knowledge of part in CAD is used to correct that of the corresponding part in observation. The likelihood maximization estimation is used to infer the accurate poses and assembly relations based on the probabilistic assembly graph. Findings In the experiments, both simulated data and real-world data are applied to evaluate the performance of the PAGC* model. The experimental results show the superiority of PAGC* in accuracy compared with assembly graph (AG) and probabilistic assembly graph without CAD model (PAG). Originality/value The paper provides a new approach to get the accurate pose of each part in demonstration stage of the robot PbD system. By integrating information from visual observation with prior knowledge from CAD model, PAGC* ensures the success in execution stage of the PbD system.

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