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1. The Elliott and Connolly Benchmark: A Test for Evaluating the In-Hand Dexterity of Robot Hands

   Coulson, R. ; Li, C. ; Majidi, C. ; Pollard, N. ( April 2021 , IEEERAS International Conference on Humanoid Robots)

   null (Ed.)

   Achieving dexterous in-hand manipulation with robot hands is an extremely challenging problem, in part due to current limitations in hardware design. One notable bottleneck hampering the development of improved hardware for dexterous manipulation is the lack of a standardized benchmark for evaluating in-hand dexterity. In order to address this issue, we establish a new benchmark for evaluating in- hand dexterity, specifically for humanoid type robot hands: the Elliott and Connolly Benchmark. This benchmark is based on a classification of human manipulations established by Elliott and Connolly, and consists of 13 distinct in-hand manipulation patterns. We define qualitative and quantitative metrics for evaluation of the benchmark, and provide a detailed testing protocol. Additionally, we introduce a dexterous robot hand - the CMU Foam Hand III - which is evaluated using the benchmark, successfully completing 10 of the 13 manipulation patterns and outperforming human hand baseline results for several of the patterns. 

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2. Generative Attention Learning: a “GenerAL” framework for high-performance multi-fingered grasping in clutter

   https://doi.org/10.1007/s10514-020-09907-y  

   Wu, Bohan ; Akinola, Iretiayo ; Gupta, Abhi ; Xu, Feng ; Varley, Jacob ; Watkins-Valls, David ; Allen, Peter K. ( February 2020 , Autonomous Robots)

   Generative Attention Learning (GenerAL) is a framework for high-DOF multi-fingered grasping that is not only robust to dense clutter and novel objects but also effective with a variety of different parallel-jaw and multi-fingered robot hands. This framework introduces a novel attention mechanism that substantially improves the grasp success rate in clutter. Its generative nature allows the learning of full-DOF grasps with flexible end-effector positions and orientations, as well as all finger joint angles of the hand. Trained purely in simulation, this framework skillfully closes the sim-to-real gap. To close the visual sim-to-real gap, this framework uses a single depth image as input. To close the dynamics sim-to-real gap, this framework circumvents continuous motor control with a direct mapping from pixel to Cartesian space inferred from the same depth image. Finally, this framework demonstrates inter-robot generality by achieving over 92% real-world grasp success rates in cluttered scenes with novel objects using two multi-fingered robotic hand-arm systems with different degrees of freedom. 

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3. A Low-Cost Compliant Gripper Using Cooperative Mini-Delta Robots for Dexterous Manipulation

   Mannam, P. ; Rudich, A. ; Zhang, Kevin L. ; Veloso, M. ; Kroemer, O. ; Temel, Z. ( July 2021 , Robotics science and systems)

   null (Ed.)

   Traditional parallel-jaw grippers are insufficient for delicate object manipulation due to their stiffness and lack of dexterity. Other dexterous robotic hands often have bulky fingers, rely on complex time-varying cable drives, or are prohibitively expensive. In this paper, we introduce a novel low-cost compliant gripper with two centimeter-scaled 3-DOF delta robots using off-the-shelf linear actuators and 3D-printed soft materials. To model the kinematics of delta robots with soft compliant links, which diverge from typical rigid links, we train neural networks using a perception system. Furthermore, we analyze the delta robot’s force profile by varying the starting position in its workspace and measuring the resulting force from a push action. Finally, we demonstrate the compliance and dexterity of our gripper through six dexterous manipulation tasks involving small and delicate objects. Thus, we present the groundwork for creating modular multi-fingered hands that can execute precise and low-inertia manipulations. 

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4. Geometric In-Hand Regrasp Planning: Alternating Optimization of Finger Gaits and In-Grasp Manipulation

   https://doi.org/10.1109/ICRA.2018.8460496  

   Sundaralingam, Balakumar ; Hermans, Tucker ( May 2018 , 10.1109/ICRA.2018.8460496)

   This paper explores the problem of autonomous, in-hand regrasping-the problem of moving from an initial grasp on an object to a desired grasp using the dexterity of a robot's fingers. We propose a planner for this problem which alternates between finger gaiting, and in-grasp manipulation. Finger gaiting enables the robot to move a single finger to a new contact location on the object, while the remaining fingers stably hold the object. In-grasp manipulation moves the object to a new pose relative to the robot's palm, while maintaining the contact locations between the hand and object. Given the object's geometry (as a mesh), the hand's kinematic structure, and the initial and desired grasps, we plan a sequence of finger gaits and object reposing actions to reach the desired grasp without dropping the object. We propose an optimization based approach and report in-hand regrasping plans for 5 objects over 5 in-hand regrasp goals each. The plans generated by our planner are collision free and guarantee kinematic feasibility. 

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5. Journey from human hands to robot hands: biological inspiration of anthropomorphic robotic manipulators

   https://doi.org/10.1088/1748-3190/ad262c  

   Han, Michael Seokyoung ; Harnett, Cindy K. ( February 2024 , Bioinspiration & Biomimetics)

   The development of robotic hands that can replicate the complex movements and dexterity of the human hand has been a longstanding challenge for scientists and engineers. A human hand is capable of not only delicate operation but also crushing with power. For performing tasks alongside and in place of humans, an anthropomorphic manipulator design is considered the most advanced implementation, because it is able to follow humans’ examples and use tools designed for people. In this article, we explore the journey from human hands to robot hands, tracing the historical advancements and current state-of-the-art in hand manipulator development. We begin by investigating the anatomy and function of the human hand, highlighting the bone-tendon-muscle structure, skin properties, and motion mechanisms. We then delve into the field of robotic hand development, focusing on highly anthropomorphic designs. Finally, we identify the requirements and directions for achieving the next level of robotic hand technology.

    

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