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

  • Ravi Balasubramanian
  • Ling Xu
  • Peter D. Brook
  • Joshua R. Smith
  • Yoky Matsuoka
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 95)


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.


Grasping Haptic interface Human-robot interaction Manipulators Telerobotics 



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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ravi Balasubramanian
    • 1
  • Ling Xu
    • 2
  • Peter D. Brook
    • 3
  • Joshua R. Smith
    • 3
  • Yoky Matsuoka
    • 3
  1. 1.School of Mechanical, Industrial, and Manufacturing EngineeringOregon State UniversityCorvallisUSA
  2. 2.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of Computer ScienceThe University of WashingtonSeattleUSA

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