Automated assembly skill acquisition through human demonstration

Gu, Yu; Sheng, Weihua; Ou, Yongsheng

doi:10.1109/icra.2014.6907790

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…Researchers have explored methods to improve a robot's performance, such as working with more complex parts with force-guided assembly (Dietrich et al, 2010) and increasing the speed of compliant manipulators (Bös et al, 2017) (For a review on robotic assembly with learning from demonstration, see Zhu and Hu, 2018). Beyond the peg-in-hole task, previous studies also considered the slide-in-the-groove assembly task (Peternel et al, 2015), and robot assembly that leveraged tool use such as hammering and wrenching (Gu et al, 2014). Nair et al (2019a,b) studied tool manufacturing by combining available parts.…”

Section: Tool Manufacturingmentioning

confidence: 99%

Robot tool use: A survey

Qin

Brawer²,

Scassellati³

2023

Front. Robot. AI

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Using human tools can significantly benefit robots in many application domains. Such ability would allow robots to solve problems that they were unable to without tools. However, robot tool use is a challenging task. Tool use was initially considered to be the ability that distinguishes human beings from other animals. We identify three skills required for robot tool use: perception, manipulation, and high-level cognition skills. While both general manipulation tasks and tool use tasks require the same level of perception accuracy, there are unique manipulation and cognition challenges in robot tool use. In this survey, we first define robot tool use. The definition highlighted the skills required for robot tool use. The skills coincide with an affordance model which defined a three-way relation between actions, objects, and effects. We also compile a taxonomy of robot tool use with insights from animal tool use literature. Our definition and taxonomy lay a theoretical foundation for future robot tool use studies and also serve as practical guidelines for robot tool use applications. We first categorize tool use based on the context of the task. The contexts are highly similar for the same task (e.g., cutting) in non-causal tool use, while the contexts for causal tool use are diverse. We further categorize causal tool use based on the task complexity suggested in animal tool use studies into single-manipulation tool use and multiple-manipulation tool use. Single-manipulation tool use are sub-categorized based on tool features and prior experiences of tool use. This type of tool may be considered as building blocks of causal tool use. Multiple-manipulation tool use combines these building blocks in different ways. The different combinations categorize multiple-manipulation tool use. Moreover, we identify different skills required in each sub-type in the taxonomy. We then review previous studies on robot tool use based on the taxonomy and describe how the relations are learned in these studies. We conclude with a discussion of the current applications of robot tool use and open questions to address future robot tool use.

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Section: Tool Manufacturingmentioning

confidence: 99%

Robot tool use: A survey

Qin

Brawer²,

Scassellati³

2023

Front. Robot. AI

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“…Kikuchi et al 38 presented a motion analysis method to extract tacit knowledge, such as expert hand gestures and eye movements, during a composite layup task and their relationship with mechanical properties and dimensional stability of the resulting product. Gu et al 39 developed a Portable Assembly Demonstration (PAD) system using an RGB-D camera, this system could recognize the tool/part used, the action applied and the assembly state characterizing the spatial relationship between the parts. Chen et al 40 proposed a fusion framework that utilised data from two differing modality sensors (a Kinect camera and a wearable inertial sensor (accelerometer)) to extract and analyse operator actions during a manual task.…”

Section: State Of the Artmentioning

confidence: 99%

In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions

Tiwari

Farnsworth

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Manual operations feature prominently in the manufacture of many electrical machines. Even though high-volume electrical machine manufacture is dominated by automation, several manufacturing operations continue to involve manual intervention because of the complexity of such operations makes them heavily reliant on high dexterity manual skills and experience. However, quality can be variable due to human involvement. Currently, in order to maintain a high precision of control and required tolerances of the final machine, inspection is performed at various steps during manufacturing and assembly. Detecting a defect at these end-of-line tests can result in significant wasted time and costs due to rework or scrappage. The solution to this problem lies in in-process monitoring particularly for error prone manual operations. This paper presents a literature review of the state-of-the-art available techniques and limitations in process monitoring within the context of electrical machine manufacturing. To quantify the degree of manual activities in process monitoring within electrical machine manufacture, a structured survey of UK based companies was conducted, identifying specific error prone manual processes to target, and gaps in inspection. The survey identified that a significant proportion of activities in electrical machine manufacture are manual, or semi-automated with manual interventions. However, literature review revealed only a limited research in in-process monitoring of manual operations in this area. Finally, two case studies are presented where case study 1 presents a framework for digitisation of a variety of manual manufacturing tasks, and case study 2 demonstrates real-time capture, modelling and analysis of deformable linear objects in electrical machine manufacturing.

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