1,433 research outputs found
Grasping and Control Issues in Adaptive End Effectors
Research into robotic grasping and manipulation has led to the development of a large number of tendon based end effectors. Many are, however, developed as a research tool, which are limited in application to the laboratory environment. The main reason being that the designs requiring a large number of actuators to be controlled. Due to the space and safety requirements, very few have been developed and commissioned for industrial applications. This paper presents design of a rigid link finger operated by a minimum number of actuators, which may be suitable for a number of adaptive end effectors. The adaptive nature built into the end effector (due to limited number of actuators) presents considerable problems in grasping and control. The paper discusses the issues associated with such designs. The research can be applicable to any adaptive end effectors that are controlled by limited number of actuators and evaluates their suitability in industrial environments
Real-Time Online Re-Planning for Grasping Under Clutter and Uncertainty
We consider the problem of grasping in clutter. While there have been motion
planners developed to address this problem in recent years, these planners are
mostly tailored for open-loop execution. Open-loop execution in this domain,
however, is likely to fail, since it is not possible to model the dynamics of
the multi-body multi-contact physical system with enough accuracy, neither is
it reasonable to expect robots to know the exact physical properties of
objects, such as frictional, inertial, and geometrical. Therefore, we propose
an online re-planning approach for grasping through clutter. The main challenge
is the long planning times this domain requires, which makes fast re-planning
and fluent execution difficult to realize. In order to address this, we propose
an easily parallelizable stochastic trajectory optimization based algorithm
that generates a sequence of optimal controls. We show that by running this
optimizer only for a small number of iterations, it is possible to perform real
time re-planning cycles to achieve reactive manipulation under clutter and
uncertainty.Comment: Published as a conference paper in IEEE Humanoids 201
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
Tactile-based Object Retrieval From Granular Media
We introduce GEOTACT, a robotic manipulation method capable of retrieving
objects buried in granular media. This is a challenging task due to the need to
interact with granular media, and doing so based exclusively on tactile
feedback, since a buried object can be completely hidden from vision. Tactile
feedback is in itself challenging in this context, due to ubiquitous contact
with the surrounding media, and the inherent noise level induced by the tactile
readings. To address these challenges, we use a learning method trained
end-to-end with simulated sensor noise. We show that our problem formulation
leads to the natural emergence of learned pushing behaviors that the
manipulator uses to reduce uncertainty and funnel the object to a stable grasp
despite spurious and noisy tactile readings. We also introduce a training
curriculum that enables learning these behaviors in simulation, followed by
zero-shot transfer to real hardware. To the best of our knowledge, GEOTACT is
the first method to reliably retrieve a number of different objects from a
granular environment, doing so on real hardware and with integrated tactile
sensing. Videos and additional information can be found at
https://jxu.ai/geotact
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
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Haptic Perception with a Robot Hand: Requirements and Realization
This paper first discusses briefly some of the recent ideas of perceptual psychology on the human haptic system particularly those of J.J. Gibson and Klatzky and Lederman. Following this introduction, we present some of the requirements of robotic haptic sensing and the results of experiments using a Utah/MIT dexterous robot hand to derive geometric object information using active sensing
Survey on model-based manipulation planning of deformable objects
A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modelling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.Preprin
A survey of dextrous manipulation
technical reportThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably humanlike manipulators to perform the deft movement that we take for granted in the human hand. The kinematic and control issues surrounding manipulation research are clouded by more basic concerns such as: what is the goal of a manipulation system, is the anthropomorphic or functional design methodology appropriate, and to what degree does the control of the manipulator depend on other sensory systems. This paper examines the potential of creating a general purpose, anthropomorphically motivated, dextrous manipulation system. The discussion will focus on features of the human hand that permit its general usefulness as a manipulator. A survey of machinery designed to emulate these capabilities is presented. Finally, the tasks of grasping and manipulation are examined from the control standpoint to suggest a control paradigm which is descriptive, yet flexible and computationally efficient1
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