216 research outputs found
From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor
Deep learning has the potential to have the impact on robot touch that it has
had on robot vision. Optical tactile sensors act as a bridge between the
subjects by allowing techniques from vision to be applied to touch. In this
paper, we apply deep learning to an optical biomimetic tactile sensor, the
TacTip, which images an array of papillae (pins) inside its sensing surface
analogous to structures within human skin. Our main result is that the
application of a deep CNN can give reliable edge perception and thus a robust
policy for planning contact points to move around object contours. Robustness
is demonstrated over several irregular and compliant objects with both tapping
and continuous sliding, using a model trained only by tapping onto a disk.
These results relied on using techniques to encourage generalization to tasks
beyond which the model was trained. We expect this is a generic problem in
practical applications of tactile sensing that deep learning will solve. A
video demonstrating the approach can be found at
https://www.youtube.com/watch?v=QHrGsG9AHtsComment: Accepted in RAL and ICRA 2019. N. Lepora and J. Lloyd contributed
equally to this wor
TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors
Tactile sensors provide useful contact data during the interaction with an
object which can be used to accurately learn to determine the stability of a
grasp. Most of the works in the literature represented tactile readings as
plain feature vectors or matrix-like tactile images, using them to train
machine learning models. In this work, we explore an alternative way of
exploiting tactile information to predict grasp stability by leveraging
graph-like representations of tactile data, which preserve the actual spatial
arrangement of the sensor's taxels and their locality. In experimentation, we
trained a Graph Neural Network to binary classify grasps as stable or slippery
ones. To train such network and prove its predictive capabilities for the
problem at hand, we captured a novel dataset of approximately 5000
three-fingered grasps across 41 objects for training and 1000 grasps with 10
unknown objects for testing. Our experiments prove that this novel approach can
be effectively used to predict grasp stability
Grasping and Manipulation of Unknown Objects Based on Visual and Tactile Feedback
Haschke R. Grasping and Manipulation of Unknown Objects Based on Visual and Tactile Feedback. In: Carbone G, Gomez-Bravo F, eds. Motion and Operation Planning of Robotic Systems. Mechanisms and Machine Science. Vol 29. Switzerland: Springer; 2015: 522.The sense of touch allows humans and higher animals to perform coordinated and efficient interactions within their environment. Recently, tactile sensor arrays providing high force, spatial, and temporal resolution became available
for robotics, which allows us to consider new control strategies to exploit this important and valuable sensory channel for grasping and manipulation tasks. Successful
dexterous manipulation strongly depends on tight feedback loops integrating proprioceptive, visual, and tactile feedback. We introduce a framework for tactile servoing
that can realize specific tactile interaction patterns, for example to establish and maintain contact (grasping) or to explore and manipulate objects. We demonstrate and
evaluate the capabilities of the proposed control framework in a series of preliminary experiments employing a 16 Ć 16 tactile sensor array attached to a Kuka LWR arm as a large fingertip
The TacTip Family : Soft Optical Tactile Sensors with 3D-Printed Biomimetic Morphologies
The authors thank Sam Coupland, Gareth Griffiths, and Samuel Forbes for their help with 3D printing and Jason Welsby for his assistance with electronics. N.L. was supported, in part, by a Leverhulme Trust Research Leadership Award on āA biomimetic forebrain for robot touchā (RL-2016-039), and N.L. and M.E.G. were supported, in part, by an EPSRC grant on Tactile Super-resolution Sensing (EP/M02993X/1). L.C. was supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE).Peer reviewedPublisher PD
On the Evolutionary Co-Adaptation of Morphology and Distributed Neural Controllers in Adaptive Agents
The attempt to evolve complete embodied and situated artiļ¬cial creatures in which
both morphological and control characteristics are adapted during the evolutionary
process has been and still represents a long term goal key for the artiļ¬cial life and
the evolutionary robotics community.
Loosely inspired by ancient biological organisms which are not provided with a
central nervous system and by simple organisms such as stick insects, this thesis
proposes a new genotype encoding which allows development and evolution of mor-
phology and neural controller in artiļ¬cial agents provided with a distributed neural
network.
In order to understand if this kind of network is appropriate for the evolution of
non trivial behaviours in artiļ¬cial agents, two experiments (description and results
will be shown in chapter 3) in which evolution was applied only to the controllerās
parameters were performed.
The results obtained in the ļ¬rst experiment demonstrated how distributed neural
networks can achieve a good level of organization by synchronizing the output of
oscillatory elements exploiting acceleration/deceleration mechanisms based on local
interactions.
In the second experiment few variants on the topology of neural architecture were
introduced. Results showed how this new control system was able to coordinate the
legs of a simulated hexapod robot on two diļ¬erent gaits on the basis of the external
circumstances.
After this preliminary and successful investigation, a new genotype encoding able to
develop and evolve artiļ¬cial agents with no ļ¬xed morphology and with a distributed
neural controller was proposed. A second set of experiments was thus performed
and the results obtained conļ¬rmed both the eļ¬ectiveness of genotype encoding and
the ability of distributed neural network to perform the given task.
The results have also shown the strength of genotype both in generating a wide
range of diļ¬erent morphological structures and in favouring a direct co-adaptation
between neural controller and morphology during the evolutionary process.
Furthermore the simplicity of the proposed model has showed the eļ¬ective role of
speciļ¬c elements in evolutionary experiments. In particular it has demonstrated the
importance of the environment and its complexity in evolving non-trivial behaviours
and also how adding an independent component to the ļ¬tness function could help
the evolutionary process exploring a larger space solutions avoiding a premature
convergence towards suboptimal solutions
Learning to stop: a unifying principle for legged locomotion in varying environments.
Evolutionary studies have unequivocally proven the transition of living organisms from water to land. Consequently, it can be deduced that locomotion strategies must have evolved from one environment to the other. However, the mechanism by which this transition happened and its implications on bio-mechanical studies and robotics research have not been explored in detail. This paper presents a unifying control strategy for locomotion in varying environments based on the principle of 'learning to stop'. Using a common reinforcement learning framework, deep deterministic policy gradient, we show that our proposed learning strategy facilitates a fast and safe methodology for transferring learned controllers from the facile water environment to the harsh land environment. Our results not only propose a plausible mechanism for safe and quick transition of locomotion strategies from a water to land environment but also provide a novel alternative for safer and faster training of robots
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