Soft Hands

Robust Proprioceptive Grasping with a Soft Robot Hand

We developed a soft hand capable of robustly grasping and identifying objects using internal strain and force measurements as well as computer vision.

A soft hand that is capable of robustly grasping and identifying objects based on internal state measurements along with a system that autonomously performs grasps. The highly compliant soft hand allows for intrinsic robustness to grasping uncertainties. The addition of internal sensing allows the configuration of the hand and object to be detected. The finger module includes resistive force sensors on the fingertips for contact detection and resistive bend sensors for measuring the curvature profile of the finger. The curvature sensors can be used to estimate the contact geometry. This capability allows to distinguish between a set of grasped objects. With one data point from each finger, the object can be identified by grasping it. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps.

  • [PDF] [DOI] B. S. Homberg, R. K. Katzschmann, M. R. Dogar, and D. Rus, “Haptic Identification of Objects using a Modular Soft Robotic Gripper,” in Intelligent robots and systems (iros), 2015 ieee/rsj international conference on, 2015, pp. 1698-1705.
    [Bibtex]
    @inproceedings{homberg2015haptic,
    abstract = {This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements. A highly compliant hand allows for intrinsic robustness to grasping uncertainty, but the specific configuration of the hand and object is not known, leaving undetermined if a grasp was successful in picking up the right object. A soft finger was adapted and combined to form a three finger gripper that can easily be attached to existing robots, for example, to the wrist of the Baxter robot. Resistive bend sensors were added within each finger to provide a configuration estimate sufficient for distinguishing between a set of objects. With one data point from each finger, the object grasped by the gripper can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. This hand is a first step towards robust proprioceptive soft grasping.},
    author = {Homberg, Bianca S and Katzschmann, Robert K and Dogar, Mehmet R and Rus, Daniela},
    booktitle = {Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on},
    doi = {10.1109/IROS.2015.7353596},
    keywords = {Baxter robot,Grasping,Grippers,Object recognition,Robot sensing systems,Rubber,clustering algorithm,dexterous manipulators,enveloping grasps,grippers,highly compliant hand,internal state measurements,modular soft robotic gripper,object haptic identification,pattern clustering,pinch grasps,resistive bend sensors,robust proprioceptive soft grasping,three finger gripper},
    month = {sep},
    pages = {1698--1705},
    title = {{Haptic Identification of Objects using a Modular Soft Robotic Gripper}},
    year = {2015}
    }

Link to Soft Robotic Hand Project at the Distributed Robotics Lab, CSAIL, MIT