842 research outputs found
A compliance-centric view of grasping
We advocate the central importance of compliance for grasp performance and demonstrate that grasp algorithms can achieve robust performance by explicitly considering and exploiting mechanical compliance of the grasping hand. Specifically, we consider the problem of robust grasping in the absence of a priori object models, focusing on object capture and grasp stability under variations of object shape for a given robotic hand. We present a simple characterization of the relationship between hand compliance, object shape, and grasp success. Based on this hypothesis, we devise a compliance-centric grasping algorithm. Real-world experiments show that this algorithm outperforms compliance-agnostic grasping, eliminates the need for explicit contact state planning, and simplifies the perceptual requirements when no a priori information about the environment is available.EC/FP7/248258/EU/Flexible Skill Acquisition and Intuitive Robot Tasking for Mobile Manipulation in the Real World/FIRST-M
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video
Sensory Communication
Contains table of contents for Section 2 and reports on five research projects.National Institutes of Health Contract 2 R01 DC00117National Institutes of Health Contract 1 R01 DC02032National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Contract N01 DC22402National Institutes of Health Grant R01-DC001001National Institutes of Health Grant R01-DC00270National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Air Warfare Center Training Systems Division Contract N61339-94-C-0087U.S. Navy - Naval Air Warfare Center Training System Division Contract N61339-93-C-0055U.S. Navy - Office of Naval Research Grant N00014-93-1-1198National Aeronautics and Space Administration/Ames Research Center Grant NCC 2-77
Sensory Communication
Contains table of contents for Section 2, an introduction and reports on twelve research projects.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R01-DC00270U.S. Air Force - Office of Scientific Research Contract AFOSR-90-0200National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Training Systems Center Contract N61339-93-M-1213U.S. Navy - Naval Training Systems Center Contract N61339-93-C-0055U.S. Navy - Naval Training Systems Center Contract N61339-93-C-0083U.S. Navy - Office of Naval Research Grant N00014-92-J-4005U.S. Navy - Office of Naval Research Grant N00014-93-1-119
Sensory Communication
Contains table of contents for Section 2, an introduction and reports on fifteen research projects.National Institutes of Health Grant RO1 DC00117National Institutes of Health Grant RO1 DC02032National Institutes of Health Contract P01-DC00361National Institutes of Health Contract N01-DC22402National Institutes of Health/National Institute on Deafness and Other Communication Disorders Grant 2 R01 DC00126National Institutes of Health Grant 2 R01 DC00270National Institutes of Health Contract N01 DC-5-2107National Institutes of Health Grant 2 R01 DC00100U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-94-C-0087U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-95-K-0014U.S. Navy - Office of Naval Research/Naval Air Warfare Center Grant N00014-93-1-1399U.S. Navy - Office of Naval Research/Naval Air Warfare Center Grant N00014-94-1-1079U.S. Navy - Office of Naval Research Subcontract 40167U.S. Navy - Office of Naval Research Grant N00014-92-J-1814National Institutes of Health Grant R01-NS33778U.S. Navy - Office of Naval Research Grant N00014-88-K-0604National Aeronautics and Space Administration Grant NCC 2-771U.S. Air Force - Office of Scientific Research Grant F49620-94-1-0236U.S. Air Force - Office of Scientific Research Agreement with Brandeis Universit
Deep Learning of Force Manifolds from the Simulated Physics of Robotic Paper Folding
Robotic manipulation of slender objects is challenging, especially when the
induced deformations are large and nonlinear. Traditionally, learning-based
control approaches, such as imitation learning, have been used to address
deformable material manipulation. These approaches lack generality and often
suffer critical failure from a simple switch of material, geometric, and/or
environmental (e.g., friction) properties. This article tackles a fundamental
but difficult deformable manipulation task: forming a predefined fold in paper
with only a single manipulator. A data-driven framework combining
physically-accurate simulation and machine learning is used to train a deep
neural network capable of predicting the external forces induced on the
manipulated paper given a grasp position. We frame the problem using scaling
analysis, resulting in a control framework robust against material and
geometric changes. Path planning is then carried out over the generated "neural
force manifold" to produce robot manipulation trajectories optimized to prevent
sliding, with offline trajectory generation finishing 15 faster than
previous physics-based folding methods. The inference speed of the trained
model enables the incorporation of real-time visual feedback to achieve
closed-loop sensorimotor control. Real-world experiments demonstrate that our
framework can greatly improve robotic manipulation performance compared to
state-of-the-art folding strategies, even when manipulating paper objects of
various materials and shapes.Comment: Supplementary video is available on YouTube:
https://youtu.be/k0nexYGy-P
Active haptic perception in robots: a review
In the past few years a new scenario for robot-based applications has emerged. Service
and mobile robots have opened new market niches. Also, new frameworks for shop-floor
robot applications have been developed. In all these contexts, robots are requested to
perform tasks within open-ended conditions, possibly dynamically varying. These new
requirements ask also for a change of paradigm in the design of robots: on-line and safe
feedback motion control becomes the core of modern robot systems. Future robots will
learn autonomously, interact safely and possess qualities like self-maintenance. Attaining
these features would have been relatively easy if a complete model of the environment
was available, and if the robot actuators could execute motion commands perfectly
relative to this model. Unfortunately, a complete world model is not available and robots
have to plan and execute the tasks in the presence of environmental uncertainties which
makes sensing an important component of new generation robots. For this reason,
today\u2019s new generation robots are equipped with more and more sensing components,
and consequently they are ready to actively deal with the high complexity of the real
world. Complex sensorimotor tasks such as exploration require coordination between the
motor system and the sensory feedback. For robot control purposes, sensory feedback
should be adequately organized in terms of relevant features and the associated data
representation. In this paper, we propose an overall functional picture linking sensing
to action in closed-loop sensorimotor control of robots for touch (hands, fingers). Basic
qualities of haptic perception in humans inspire the models and categories comprising the
proposed classification. The objective is to provide a reasoned, principled perspective on
the connections between different taxonomies used in the Robotics and human haptic
literature. The specific case of active exploration is chosen to ground interesting use
cases. Two reasons motivate this choice. First, in the literature on haptics, exploration has
been treated only to a limited extent compared to grasping and manipulation. Second,
exploration involves specific robot behaviors that exploit distributed and heterogeneous
sensory data
On Model Adaptation for Sensorimotor Control of Robots
International audienceIn this expository article, we address the problem of computing adaptive models that can be used for guiding the motion of robotic systems with uncertain action-to-perception relations. The formulation of the uncalibrated sensor-based control problem is first presented, then, various methods for building adaptive sensorimotor models are derived and analysed. Finally, the proposed methodology is exemplified with two cases of study
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