Robotic handwriting: Multi-contact manipulation based on Reactional Internal Contact Hypothesis

Kim, Sung-Kyun; Jo, Joonhee; Oh, Yonghwan; Oh, Sang-Rok; Srinivasa, Siddhartha S.; Likhachev, Maxim

doi:10.1109/iros.2014.6942663

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…The purpose of non-causal tool use is to duplicate or reproduce actions with limited variations. It could be achieved by programming a wide variety of tool use actions such as nut fastening in aircraft production that requires high precision ( Pfeiffer et al., 2017 ), stub grinding and deburring with force control ( Robertsson et al., 2006 ), handwriting that involves multi-contact manipulation ( Kim et al., 2014 ), furniture polishing that uses an impedance model ( Nagata et al., 2001 ), generating a collision-free polishing path ( Takeuchi et al., 1993 ), accurately drawing a circle with a compass that involves complex contacts ( Kutsuzawa et al., 2017 ), unfastening screws in collaborative tasks ( Li et al., 2020 ), and pouring based on the volume of liquid ( Rozo et al., 2013 ), or actions relevant to tool use such as grasping a knife resting on a cutting board that requires a high level of dexterity ( Xue and Jia, 2020 ), or segmenting a surgical tool from the background while using it ( Garcia-Peraza-Herrera et al., 2017 ; Su et al., 2018 ). The purpose of these approaches is to automate one process to facilitate human work.…”

Section: Robot Tool Use Literaturementioning

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: Robot Tool Use Literaturementioning

confidence: 99%

Robot tool use: A survey

Qin

Brawer²,

Scassellati³

2023

Front. Robot. AI

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“…A Chinese calligraphy robot that rely on the calligraphy database was designed in work [3]. The manipulation accuracy and force feedback ability were considered to control the writing pressures of the robotic manipulator [4]. Machine vision technologies and various visual sensors are applied to obtain the visual feedback and better trajectory planning performance [5].…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

Wang¹,

Yang

Wang

et al. 2023

CAAI Trans on Intel Tech

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Robot calligraphy visually reflects the motion capability of robotic manipulators. While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters, this article presents a generative adversarial network (GAN)‐based motion learning method for robotic calligraphy synthesis (Gan2CS) that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works. The key technologies in the proposed approach include: (1) adopting the GAN to learn the motion parameters from the robot writing operation; (2) converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration; (3) reproducing high‐precision calligraphy works by synthesising the writing motion data hierarchically. In this study, the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module. The robot performs the writing with motion planning, and the writing motion parameters of calligraphy strokes are learnt with GANs. Then the motion data of basic strokes is synthesised based on the hierarchical process of ‘stroke‐radical‐part‐character’. And the robot re‐writes the synthesised characters whose similarity with the original calligraphy characters is evaluated. Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

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“…KW Kwok did a great deal of work on a fivedegree-of-freedom manipulator and added machine vision to correct errors in writing in real time. Kim S K studied pen writing and writing intensity [7].…”

Section: Introductionmentioning

confidence: 99%

Implementing Multi-DOF Trajectory Tracking Control System for Robotic Arm Experimental Platform

Yang²,

Li³

et al. 2018

2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

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To implement the control system of a multi-DOF robotic manipulator (Dobot), the robot dynamics, trajectory planning algorithm and motion control strategy are studied for designing the trajectory tracking control system. In this paper, the hardware and software of Dobot magician control system are designed. The hardware mainly includes STM32 controller. The software part mainly builds the host computer display interface, completes the protocol communication between the robot manipulator and the PC, so as to realize the trajectory tracking control of the robot manipulator and implement the trackfollowing in real time. The experimental results show that the control system can accurately track the trajectory of robotic manipulator with a certain degree of real-time and stability.

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Robotic handwriting: Multi-contact manipulation based on Reactional Internal Contact Hypothesis

Cited by 6 publications

References 24 publications

Robot tool use: A survey

Robot tool use: A survey

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

Implementing Multi-DOF Trajectory Tracking Control System for Robotic Arm Experimental Platform

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