Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace

Human-in-the-loop manipulation is useful in when autonomous grasping is not able to deal sufficiently well with corner cases or cannot operate fast enough. Using the teleoperator's hand as an input device can provide an intuitive control method but requires mapping between pose spaces which may not be similar. We propose a low-dimensional and continuous teleoperation subspace which can be used as an intermediary for mapping between different hand pose spaces. We present an algorithm to project between pose space and teleoperation subspace. We use a non-anthropomorphic robot to experimentally prove that it is possible for teleoperation subspaces to effectively and intuitively enable teleoperation. In experiments, novice users completed pick and place tasks significantly faster using teleoperation subspace mapping than they did using state of the art teleoperation methods.Comment: ICRA 2018, 7 pages, 7 figures, 2 table

The Anthropomorphic Hand Assessment Protocol (AHAP)

The progress in the development of anthropomorphic hands for robotic and prosthetic applications has not been followed by a parallel development of objective methods to evaluate their performance. The need for benchmarking in grasping research has been recognized by the robotics community as an important topic. In this study we present the Anthropomorphic Hand Assessment Protocol (AHAP) to address this need by providing a measure for quantifying the grasping ability of artificial hands and comparing hand designs. To this end, the AHAP uses 25 objects from the publicly available Yale-CMU-Berkeley Object and Model Set thereby enabling replicability. It is composed of 26 postures/tasks involving grasping with the eight most relevant human grasp types and two non-grasping postures. The AHAP allows to quantify the anthropomorphism and functionality of artificial hands through a numerical Grasping Ability Score (GAS). The AHAP was tested with different hands, the first version of the hand of the humanoid robot ARMAR-6 with three different configurations resulting from attachment of pads to fingertips and palm as well as the two versions of the KIT Prosthetic Hand. The benchmark was used to demonstrate the improvements of these hands in aspects like the grasping surface, the grasp force and the finger kinematics. The reliability, consistency and responsiveness of the benchmark have been statistically analyzed, indicating that the AHAP is a powerful tool for evaluating and comparing different artificial hand designs

Smart hands for the EVA retriever

Dexterous, robotic hands are required for the extravehicular activity retriever (EVAR) system being developed by the NASA Johnson Space Center (JSC). These hands, as part of the EVAR system, must be able to grasp objects autonomously and securely which inadvertently separate from the Space Station. Development of the required hands was initiated in 1987. Outlined here are the hand development activities, including design considerations, progress to date, and future plans. Several types of dexterous hands that were evaluated, along with a proximity-sensing capability that was developed to initiate a reflexive, adaptive grasp, are described. The evaluations resulted in the design and fabrication of a 6-degree-of-freedom (DOF) hand that has two fingers and a thumb arranged in an anthropomorphic configuration. Finger joint force and position sensors are included in the design, as well as infrared proximity sensors which allow initiation of the grasp sequence when an object is detected within the grasp envelope

Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot

Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and an anthropomorphic upper body to carry out complex tasks in environments too dangerous for humans. Due to its high number of degrees of freedom, controlling the robot with direct teleoperation approaches is challenging and exhausting. Supervised autonomy approaches are promising to increase quality and speed of control while keeping the flexibility to solve unknown tasks. We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks. The integrated system was evaluated in disaster response scenarios and showed promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 201

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Generalized Anthropomorphic Functional Grasping with Minimal Demonstrations

This article investigates the challenge of achieving functional tool-use grasping with high-DoF anthropomorphic hands, with the aim of enabling anthropomorphic hands to perform tasks that require human-like manipulation and tool-use. However, accomplishing human-like grasping in real robots present many challenges, including obtaining diverse functional grasps for a wide variety of objects, handling generalization ability for kinematically diverse robot hands and precisely completing object shapes from a single-view perception. To tackle these challenges, we propose a six-step grasp synthesis algorithm based on fine-grained contact modeling that generates physically plausible and human-like functional grasps for category-level objects with minimal human demonstrations. With the contact-based optimization and learned dense shape correspondence, the proposed algorithm is adaptable to various objects in same category and a board range of robot hand models. To further demonstrate the robustness of the framework, over 10K functional grasps are synthesized to train our neural network, named DexFG-Net, which generates diverse sets of human-like functional grasps based on the reconstructed object model produced by a shape completion module. The proposed framework is extensively validated in simulation and on a real robot platform. Simulation experiments demonstrate that our method outperforms baseline methods by a large margin in terms of grasp functionality and success rate. Real robot experiments show that our method achieved an overall success rate of 79\% and 68\% for tool-use grasp on 3-D printed and real test objects, respectively, using a 5-Finger Schunk Hand. The experimental results indicate a step towards human-like grasping with anthropomorphic hands.Comment: 20 pages, 23 figures and 7 table