4,920 research outputs found
Learning Latent Space Dynamics for Tactile Servoing
To achieve a dexterous robotic manipulation, we need to endow our robot with
tactile feedback capability, i.e. the ability to drive action based on tactile
sensing. In this paper, we specifically address the challenge of tactile
servoing, i.e. given the current tactile sensing and a target/goal tactile
sensing --memorized from a successful task execution in the past-- what is the
action that will bring the current tactile sensing to move closer towards the
target tactile sensing at the next time step. We develop a data-driven approach
to acquire a dynamics model for tactile servoing by learning from
demonstration. Moreover, our method represents the tactile sensing information
as to lie on a surface --or a 2D manifold-- and perform a manifold learning,
making it applicable to any tactile skin geometry. We evaluate our method on a
contact point tracking task using a robot equipped with a tactile finger. A
video demonstrating our approach can be seen in https://youtu.be/0QK0-Vx7WkIComment: Accepted to be published at the International Conference on Robotics
and Automation (ICRA) 2019. The final version for publication at ICRA 2019 is
7 pages (i.e. 6 pages of technical content (including text, figures, tables,
acknowledgement, etc.) and 1 page of the Bibliography/References), while this
arXiv version is 8 pages (added Appendix and some extra details
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
Bio-Inspired Motion Strategies for a Bimanual Manipulation Task
Steffen JF, Elbrechter C, Haschke R, Ritter H. Bio-Inspired Motion Strategies for a Bimanual Manipulation Task. In: International Conference on Humanoid Robots (Humanoids). 2010
Structured manifolds for motion production and segmentation : a structured Kernel Regression approach
Steffen JF. Structured manifolds for motion production and segmentation : a structured Kernel Regression approach. Bielefeld (Germany): Bielefeld University; 2010
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