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Physical Human Interactive Guidance: Identifying Grasping Principles from Human-Planned Grasps

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The Human Hand as an Inspiration for Robot Hand Development

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 95))

Abstract

We present a novel and simple experimental method called Physical Human Interactive Guidance to study human-planned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose while the robot’s configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the Physical Human Interactive Guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object’s principal axes in the human-planned grasps (termed low skewness in this work), while the automated grasps used arbitrary wrist orientation. Preliminary tests shows that grasps with low skewness were significantly more robust than grasps with high skewness (77–93 %). We conclude with a detailed discussion of how the Physical Human Interactive Guidance method relates to existing methods for extracting the human principles for physical interaction.

Work was done when the authors were all at the University of Washington and Intel Labs Seattle.

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Notes

  1. 1.

    http://www.barrett.com/robot/index.htm

  2. 2.

    http://www.barrett.com/robot/index.htm

  3. 3.

    http://www.ros.org/

  4. 4.

    In this particular experiment (see Fig. 2), a white rectangular box on which the objects were placed was used to align the object. This was only incidental to this experimental set-up, and any means of repeated accurate positioning of the object will suffice.

  5. 5.

    We also placed the objects randomly in three different locations on the table (left, right, and center with respect to the robot base) to ensure that the human-planned grasps were not unduly influenced by the specificity of the arm posture required for a particular location. Since we did not find any significant differences between the grasps from different locations in terms of the robot wrist and finger posture relative to the object, we combined all the human-planned grasps from the different locations into one set to be tested by the stationary robot.

  6. 6.

    http://grasping.cs.columbia.edu/

  7. 7.

    http://www.cyberglovesystems.com/

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Acknowledgments

The authors thank Brian Mayton for help with the robot experiment set-up and Louis LeGrand for interesting discussions on grasp metrics. Gratitude is also due to Matei Ciocarlie and Peter Allen of the GraspIt! team for helping the authors use the GraspIt! code.

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Authors and Affiliations

  1. School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA

    Ravi Balasubramanian

  2. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Ling Xu

  3. Department of Computer Science, The University of Washington, Seattle, WA, USA

    Peter D. Brook, Joshua R. Smith & Yoky Matsuoka

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  1. Ravi Balasubramanian
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  2. Ling Xu
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  4. Joshua R. Smith
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  5. Yoky Matsuoka
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Correspondence to Ravi Balasubramanian .

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Editors and Affiliations

  1. Oregon State University, Corvallis, Oregon, USA

    Ravi Balasubramanian

  2. Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona, USA

    Veronica J. Santos

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Balasubramanian, R., Xu, L., Brook, P.D., Smith, J.R., Matsuoka, Y. (2014). Physical Human Interactive Guidance: Identifying Grasping Principles from Human-Planned Grasps. In: Balasubramanian, R., Santos, V. (eds) The Human Hand as an Inspiration for Robot Hand Development. Springer Tracts in Advanced Robotics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-03017-3_22

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  • DOI: https://doi.org/10.1007/978-3-319-03017-3_22

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