164 research outputs found
Exploitation of environmental constraints in human and robotic grasping
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugÀnglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints
Exploitation of environmental constraints in human and robotic grasping
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugÀnglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present support for this view found through the analysis of human grasp behavior and by showing robust robotic grasping based on constraint-exploiting grasp strategies. Furthermore, we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints
A Robust Controller for Stable 3D Pinching using Tactile Sensing
This paper proposes a controller for stable grasping of unknown-shaped
objects by two robotic fingers with tactile fingertips. The grasp is stabilised
by rolling the fingertips on the contact surface and applying a desired
grasping force to reach an equilibrium state. The validation is both in
simulation and on a fully-actuated robot hand (the Shadow Modular Grasper)
fitted with custom-built optical tactile sensors (based on the BRL TacTip). The
controller requires the orientations of the contact surfaces, which are
estimated by regressing a deep convolutional neural network over the tactile
images. Overall, the grasp system is demonstrated to achieve stable equilibrium
poses on various objects ranging in shape and softness, with the system being
robust to perturbations and measurement errors. This approach also has promise
to extend beyond grasping to stable in-hand object manipulation with multiple
fingers.Comment: 8 pages, 10 figures, 1 appendix. Accepted for publication in IEEE
Robotics and Automation Letters and in IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2021). Supplemental video:
https://youtu.be/rfQesw3FDA
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Contact sensing and grasping performance of compliant hands
Limitations in modern sensing technologies result in large errors in sensed target object geometry and location in unstructured environments. As a result, positioning a robotic end-effector includes inherent error that will often lead to unsuccessful grasps. In previous work, we demonstrated that optimized configuration, compliance, viscosity, and adaptability in the mechanical structure of a robot hand facilitates reliable grasping in unstructured environments, even with purely feedforward control of the hand. In this paper we describe the addition of a simple contact sensor to the fingerpads of the SDM Hand (Shape Deposition Manufactured Hand), which, along with a basic control algorithm, significantly expands the grasp space of the hand and reduces contact forces during the acquisition phase of the grasp. The combination of the passive mechanics of the SDM Hand along with this basic sensor suite enables positioning errors of over 5 cm in any direction. In the context of mobile manipulation, the performance demonstrated here may reduce the need for much of the complex array of sensing currently utilized on mobile platforms, greatly increase reliability, and speed task execution, which can often be prohibitively slow.Engineering and Applied Science
Learning Pregrasp Manipulation of Objects from Ungraspable Poses
In robotic grasping, objects are often occluded in ungraspable configurations
such that no pregrasp pose can be found, eg large flat boxes on the table that
can only be grasped from the side. Inspired by humans' bimanual manipulation,
eg one hand to lift up things and the other to grasp, we address this type of
problems by introducing pregrasp manipulation - push and lift actions. We
propose a model-free Deep Reinforcement Learning framework to train control
policies that utilize visual information and proprioceptive states of the robot
to autonomously discover robust pregrasp manipulation. The robot arm learns to
first push the object towards a support surface and establishes a pivot to lift
up one side of the object, thus creating a clearance between the object and the
table for possible grasping solutions. Furthermore, we show the effectiveness
of our proposed learning framework in training robust pregrasp policies that
can directly transfer from simulation to real hardware through suitable design
of training procedures, state, and action space. Lastly, we evaluate the
effectiveness and the generalisation ability of the learned policies in
real-world experiments, and demonstrate pregrasp manipulation of objects with
various size, shape, weight, and surface friction.Comment: 8 pages open access version for ICRA2020 6 pages acceptance pape
Interaction Motion Control on Tri-finger Pneumatic Grasper using Variable Convergence Rate Prescribed Performance Impedance Control with Pressure-based Force Estimator
Pneumatic robot is a fluid dynamic based robot system which possesses immense uncertainties and nonlinearities over its electrical driven counterpart. Requirement for dynamic motion handling further challenged the implemented control system on both aspects of interaction and compliance control. This study especially set to counter the unstable and inadaptable proportional motions of pneumatic robot grasper towards its environment through the employment of Variable Convergence Rate Prescribed Performance Impedance Control (VPPIC) with pressure-based force estimation (PFE). Impedance control was derived for a single finger of Tri-finger Pneumatic Grasper (TPG) robot, with improvement being subsequently made to the controllerâs output by appropriation of formulated finite-time prescribed performance control. Produced responses from exerted pressure of the maneuvered pneumatic piston were then recorded via derived PEE with adherence to both dynamics and geometry of the designated finger. Validation of the proposed method was proceeded on both circumstances of human hand as a blockage and ping-pong ball as methodical representation of a fragile object. Developed findings confirmed relatively uniform force sensing ability for both proposed PEE and load sensor as equipped to the robotâs fingertip with respect to the experimented thrusting and holding of a human hand. Sensing capacity of the estimator has also advanced beyond the fingertip to enclose its finger in entirety. Whereas stable interaction control at negligible oscillation has been exhibited from VPPIC against the standard impedance control towards gentle and compression-free handling of fragile objects. Overall positional tracking of the finger, thus, justified VPPIC as a robust mechanism for smooth operation amid and succeed direct object interaction, notwithstanding its transcendence beyond boundaries of the prescribed performance constraint
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