Hand closure model for planning top grasps with soft robotic hands

Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

[1]  Sariel Har-Peled,et al.  Efficiently approximating the minimum-volume bounding box of a point set in three dimensions , 1999, SODA '99.

[2]  Danica Kragic,et al.  Data-Driven Grasp Synthesis—A Survey , 2013, IEEE Transactions on Robotics.

[3]  Imin Kao,et al.  Computing and controlling compliance of a robotic hand , 1989, IEEE Trans. Robotics Autom..

[4]  Siddhartha S. Srinivasa,et al.  Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set , 2015, IEEE Robotics & Automation Magazine.

[5]  Manuel G. Catalano,et al.  Adaptive synergies for the design and control of the Pisa/IIT SoftHand , 2014, Int. J. Robotics Res..

[6]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Monica Malvezzi,et al.  On the use of homogeneous transformations to map human hand movements onto robotic hands , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Ken Goldberg,et al.  Learning ambidextrous robot grasping policies , 2019, Science Robotics.

[9]  Daniela Rus,et al.  Learning Object Grasping for Soft Robot Hands , 2018, IEEE Robotics and Automation Letters.

[10]  Oliver Brock,et al.  Four aspects of building robotic systems: lessons from the Amazon Picking Challenge 2015 , 2018, Auton. Robots.

[11]  Gionata Salvietti,et al.  Replicating Human Hand Synergies Onto Robotic Hands: A Review on Software and Hardware Strategies , 2018, Front. Neurorobot..

[12]  Oliver Brock,et al.  A novel type of compliant and underactuated robotic hand for dexterous grasping , 2016, Int. J. Robotics Res..

[13]  Sergey Levine,et al.  Learning dexterous manipulation for a soft robotic hand from human demonstrations , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[14]  Monica Malvezzi,et al.  Modeling compliant grasps exploiting environmental constraints , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Abhinav Gupta,et al.  Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Robert D. Howe,et al.  The Highly Adaptive SDM Hand: Design and Performance Evaluation , 2010, Int. J. Robotics Res..

[17]  Monica Malvezzi,et al.  On Grasp Quality Measures: Grasp Robustness and Contact Force Distribution in Underactuated and Compliant Robotic Hands , 2017, IEEE Robotics and Automation Letters.

[18]  Monica Malvezzi,et al.  Exploiting Robot Hand Compliance and Environmental Constraints for Edge Grasps , 2019, Front. Robot. AI.

[19]  Antonio Bicchi,et al.  Grasp planning with soft hands using Bounding Box object decomposition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[20]  Monica Malvezzi,et al.  The Closure Signature: A Functional Approach to Model Underactuated Compliant Robotic Hands , 2018, IEEE Robotics and Automation Letters.

[21]  R. Goldman.  RECOVERING THE DATA FROM THE TRANSFORMATION MATRIX , 1991 .

[22]  Wing-Kuen Ling,et al.  Robotic grasp detection using deep learning and geometry model of soft hand , 2016, 2016 IEEE International Conference on Consumer Electronics-China (ICCE-China).

[23]  SuárezRaúl,et al.  Grasp quality measures , 2015 .

[24]  Mark R. Cutkosky,et al.  On grasp choice, grasp models, and the design of hands for manufacturing tasks , 1989, IEEE Trans. Robotics Autom..

[25]  Clément Gosselin,et al.  Underactuated Robotic Hands , 2008, Springer Tracts in Advanced Robotics.

[26]  Antonio Bicchi,et al.  On Motion and Force Controllability of Precision Grasps with Hands Actuated by Soft Synergies , 2013, IEEE Transactions on Robotics.

[27]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[28]  Fumiya Iida,et al.  Soft Manipulators and Grippers: A Review , 2016, Front. Robot. AI.

[29]  Henrik I. Christensen,et al.  Automatic grasp planning using shape primitives , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[30]  Máximo A. Roa,et al.  Grasp quality measures: review and performance , 2014, Autonomous Robots.

[31]  J. F. Soechting,et al.  Postural Hand Synergies for Tool Use , 1998, The Journal of Neuroscience.

[32]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..

[33]  Gerd Hirzinger,et al.  A fast and robust grasp planner for arbitrary 3D objects , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[34]  Oliver Brock,et al.  Mobility and Manipulation , 2016, Springer Handbook of Robotics, 2nd Ed..

[35]  Danica Kragic,et al.  Minimum volume bounding box decomposition for shape approximation in robot grasping , 2008, 2008 IEEE International Conference on Robotics and Automation.

[36]  Monica Malvezzi,et al.  Mapping Synergies From Human to Robotic Hands With Dissimilar Kinematics: An Approach in the Object Domain , 2013, IEEE Transactions on Robotics.

[37]  Xinyu Liu,et al.  Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning , 2017, ArXiv.

[38]  Yoji Umetani,et al.  The Development of Soft Gripper for the Versatile Robot Hand , 1978 .

[39]  Nicholas J. Higham,et al.  Fast Polar Decomposition of an Arbitrary Matrix , 1990, SIAM J. Sci. Comput..

[40]  Mathieu Aubry,et al.  Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[41]  Kaiyu Hang,et al.  Pre-Grasp Sliding Manipulation of Thin Objects Using Soft, Compliant, or Underactuated Hands , 2019, IEEE Robotics and Automation Letters.

[42]  Antonio Bicchi,et al.  On the Closure Properties of Robotic Grasping , 1995, Int. J. Robotics Res..

[43]  Oliver Brock,et al.  Exploitation of environmental constraints in human and robotic grasping , 2015, Int. J. Robotics Res..

[44]  Matei Ciocarlie,et al.  Passive Reaction Analysis for Grasp Stability , 2018, IEEE Transactions on Automation Science and Engineering.

[45]  Xinyu Liu,et al.  Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics , 2017, Robotics: Science and Systems.

[46]  Honglak Lee,et al.  Deep learning for detecting robotic grasps , 2013, Int. J. Robotics Res..