Online and markerless motion retargeting with kinematic constraints

Dariush, Behzad; Gienger, Michael; Arumbakkam, Arjun; Goerick, Christian; Zhu, Yi; Fujimura, Kikuo

doi:10.1109/iros.2008.4651104

Cited by 36 publications

(25 citation statements)

References 16 publications

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“…The solution in [11], [12] assumes the existence of redundant circle in elbow joint that NAO does not have. A possible solution for this problem then is by using optimization based kinematic for avoiding joint limitation as in [2], [3]. Another problem is the problem of correspondence between the robot and human user.…”

Section: B Discussionmentioning

confidence: 99%

“…Specifics to our problem, human upper body motion transfer to NAO, the kinematic constraints we chose are inspired by the works of Kulpa et al [10] in computer graphics and the optimization based motion transfer [1][2][3].We choose to describe human movement as two unit vectors: A unit vector in the direction of upper arm; and a unit vector in the direction of lower arm.…”

Section: Kinematicsmentioning

confidence: 99%

“…The use of differential kinematic for solving motion transfer problem was proposed the first time by Choi and Ko [1] for solving motion transfer in the field of computer graphics. Recently, this method has been successfully employed in the field of robotics by Dariush et al [2] and Montecillno et al [3] for whole body humanoid motion transfer. We are inspired by the notion of generalized kinematic in [3] to solve motion transfer problem.…”

Section: B Differential Kinematicmentioning

confidence: 99%

See 2 more Smart Citations

Analytical Upper Body Human Motion Transfer to Naohumanoid Robot

Ibrahim¹,

Adiprawita²

2012

ijeei

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This paper proposes an analytical method to solve theproblem of upper body motion transfer from human to humanoid robot. By usinghumanoid normalized model as a model of upper body human motion, the proposed methodthen uses analytical inverse kinematics method to transfer human motion tohumanoid robot. Because the nature of analytical method that is specific to robot with particular kinematics structure, we choose the Aldebaran NAO robot as target platform. We verify our method by using offline simulation and online experiment in the robot. The offline simulation shows that our method gives exact solution, while the online experiment gives a qualitatively good result.

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Section: B Discussionmentioning

confidence: 99%

Section: Kinematicsmentioning

confidence: 99%

Section: B Differential Kinematicmentioning

confidence: 99%

See 1 more Smart Citation

Analytical Upper Body Human Motion Transfer to Naohumanoid Robot

Ibrahim¹,

Adiprawita²

2012

ijeei

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show abstract

“…Dariush et al [11,12] present a task space method that allows the user to specify a set of Cartesian task descriptors defining correspondences between points on the human body and a humanoid robot. An on-line control framework computes differential kinematics relating task variables and joint variables, and minimizes tracking error.…”

Section: Task-space Retargeting In Roboticsmentioning

confidence: 99%

A General Method for Kinematic Retargeting: Adapting Poses Between Humans and Robots

Tosun

Mead

Stengel

2014

Volume 4A: Dynamics, Vibration, and Control

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This paper presents a method for kinematic retargeting that is general to a broad class of kinematic chains. Kinematic retargeting is the adaptation of a pose or motion from one kinematic embodiment to another. Our method distinguishes itself in its ability to adapt poses to new robots with very little configuration by the user. We accomplish this by defining two general metrics for retargeting and minimizing a cost function which is the weighted sum of these two metrics. This allows the method to automatically adapt poses between source and target chains that have different link lengths and degrees of freedom.These capabilities address a specific problem in HumanRobot Interaction (HRI), where behaviors are often defined in a robot-specific manner. The ability to automatically adapt behaviors from humans to new robots, and from one robot to another, will facilitate experimental repeatability. Through simulation and experiments, we demonstrate that our method is effective in adapting poses across chains with different numbers of joints, and in adapting socially expressive gestures from a human to two very different robots.

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“…When the mapping function between input device of the master and output of the slave is ƒ: Χ→P, retargeting can be done in the ways based on joint space and Cartesian coordinates (Sousa et al, 2011;Gioioso et al, 2011). Also, the retargeting can be done by customized function and real-time tele-operation which covers the whole space instead of predefined motion (Dragan and Srinivasa, 2012;Dariush et al, 2008). Figure 1 shows the process of retargeting to convey the control command based on intuitive observation to the slave robot (manipulator).…”

Section: Tele-operating Systemmentioning

confidence: 99%

Tele-operating System of Field Robot for Cultivation Management - Vision based Tele-operating System of Robotic Smart Farming for Fruit Harvesting and Cultivation Management

Ryuh

Noh

Park

2014

Journal of Biosystems Engineering

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Purposes:This study was to validate the Robotic Smart Work System that can provides better working conditions and high productivity in unstructured environments like bio-industry, based on a tele-operation system for fruit harvesting with low cost 3-D positioning system on the laboratory level. Methods: For the Robotic Smart Work System for fruit harvesting and cultivation management in agriculture, a vision based tele-operating system and 3-D position information are key elements. This study proposed Robotic Smart Farming, an agricultural version of Robotic Smart Work System, and validated a 3-D position information system with a low cost omni camera and a laser marker system in the lab environment in order to get a vision based tele-operating system and 3-D position information. Results: The tasks like harvesting of the fixed target and cultivation management were accomplished even if there was a short time delay (30 ms ~ 100 ms). Although automatic conveyor works requiring accurate timing and positioning yield high productivity, the tele-operation with user's intuition will be more efficient in unstructured environments which require target selection and judgment. Conclusions: This system increased work efficiency and stability by considering ancillary intelligence as well as user's experience and knowhow. In addition, senior and female workers will operate the system easily because it can reduce labor and minimized user fatigue. Keywords IntroductionThe Smart Work Center, which government has recently supported, enables flexible work environments by providing physical extension and mobility to office setting. However, this Smart Work is applied only into the office setting, not into the field work such as casting and welding or agricultural production which requires heavy physical labor. Recent Robotic Smart Work (Ryuh, 2011;Dragan and Srinivasa, 2012) allows the field worker to control a robot installed at the field with poor working condition from the office; however, little research has been conducted in the area of agricultural industry which has similar working condition.The Robotic Smart Work System provides better working environments and high productivity to the farmers. Farmers accomplish the task with hands and foot depending on input information through the five senses. For this reason, the work productivity decreases with mental and physical limits and fatigue. Technology for tele-operation, which extends working setting and enhances the working energy, allows farmers to control the robot installed at the field and to accomplish agricultural works from cultivation to harvesting watching a screen in pleasant indoor. Thus, this technology is expected to solve the problems in agriculture and to present new paradigm in the area of agricultural industry. The Robotic Smart Farming System, a type of the Robotic Smart Work system applied into bio-industry, consists of a tele-operating system including a master controller and a slave robot installed at the farm. Therefore, time delay caused by the dista...

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Online and markerless motion retargeting with kinematic constraints

Cited by 36 publications

References 16 publications

Analytical Upper Body Human Motion Transfer to Naohumanoid Robot

Analytical Upper Body Human Motion Transfer to Naohumanoid Robot

A General Method for Kinematic Retargeting: Adapting Poses Between Humans and Robots

Tele-operating System of Field Robot for Cultivation Management - Vision based Tele-operating System of Robotic Smart Farming for Fruit Harvesting and Cultivation Management

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