Robotic Grasping: A Generic Neural Network Architecture

Rezzoug, Nasser; Gorce, Philippe; Ea, Lesp; Toulon, Sud

doi:10.5772/4687

Cited by 7 publications

(5 citation statements)

References 42 publications

(51 reference statements)

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“…a grasping action specified by a pose and gripper configuration, with a corresponding state space consisting of visual information. Based on a set of demonstrations, a learning algorithm, such as support vector machines (SVM) and regularized regression [4] or artificial neural networks [5], [6], [7], [8], [9], [10], learns the mapping between the visual state and the grasping action. In reinforcement learning (RL) there is no teacher to demonstrate how to perform the task, instead there is a reinforcement signal provided to the learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Grasping virtual fish: A step towards robotic deep learning from demonstration in virtual reality

Dyrstad

Mathiassen

2017

2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Abstract-We present an approach to robotic deep learning from demonstration in virtual reality, which combines a deep 3D convolutional neural network, for grasp detection from 3D point clouds, with domain randomization to generate a large training data set. The use of virtual reality (VR) enables robot learning from demonstration in a virtual environment. In this environment, a human user can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large synthetic training data set consisting of 76 000 example grasps of fish. After training the network using this data set, the network is able to guide a gripper to grasp virtual fish with good success rates. Our domain randomization approach is a step towards an efficient way to perform robotic deep learning from demonstration in virtual reality.

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Section: Introductionmentioning

confidence: 99%

Grasping virtual fish: A step towards robotic deep learning from demonstration in virtual reality

Dyrstad

Mathiassen

2017

2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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“…a grasping action specified by a pose and gripper configuration, with a corresponding state space consisting of visual information. Based on a set of demonstrations, a learning algorithm, such as support vector machines (SVM), regularized regression [4] or artificial neural networks [5], [6], [7], [8], [9], [10], learns to find or evaluate the mapping between the visual state and the grasping action. In reinforcement learning (RL) there is no supervisor to demonstrate how to perform the task, instead there is a reinforcement signal provided to the learning algorithm.…”

Section: A Related Workmentioning

confidence: 99%

Teaching a Robot to Grasp Real Fish by Imitation Learning from a Human Supervisor in Virtual Reality

Dyrstad

Øye

Stahl³

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We teach a real robot to grasp real fish, by training a virtual robot exclusively in virtual reality. Our approach implements robot imitation learning from a human supervisor in virtual reality. A deep 3D convolutional neural network computes grasps from a 3D occupancy grid obtained from depth imaging at multiple viewpoints. In virtual reality, a human supervisor can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large synthetic training data set consisting of 100 000 example grasps of fish. Using this data set for training purposes, the network is able to guide a real robot and gripper to grasp real fish with good success rates. The newly proposed domain randomization approach constitutes the first step in how to efficiently perform robot imitation learning from a human supervisor in virtual reality in a way that transfers well to the real world.

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“…In [10], the authors study the design of a controlling neural network using adaptive resonance theory. In [11], the authors developed a new method based on neural networks that allows learning multichain redundant structure configuration during grasping.…”

Section: Previous Workmentioning

confidence: 99%

Autonomous Robots and Behavior Initiators

Chang¹

2018

Human-Robot Interaction - Theory and Application

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We use an autonomous neural controller (ANC) that handles the mechanical behavior of virtual, multi-joint robots, with many moving parts and sensors distributed through the robot's body, satisfying basic Newtonian laws. As in living creatures, activities inside the robot include behavior initiators: self-activating networks that burn energy and function without external stimulus. Autonomy is achieved by mimicking the dynamics of biological brains, in resting situations, a default state network (DSN), specialized set of energy burning neurons, assumes control and keeps the robot in a safe condition, where other behaviors can be brought to use. Our ANC contains several kinds of neural nets trained with gradient descent to perform specialized jobs. The first group generates moving wave activities in the robot muscles, the second yields basic position/presence prediction information about sensors, the third acts as timing masters, empowering sequential tasks. We add a fourth category of self-activating networks that push behavior from the inside. Through evolutive methods, the composed network share clue information along a few connecting weights, producing self-motivated robots, capable of achieving noticeable self-level of competence. We show that this spirited robot interacts with humans and, through appropriate interfaces, learn complex behaviors that satisfy unknown, subjacent human expectative.

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Robotic Grasping: A Generic Neural Network Architecture

Cited by 7 publications

References 42 publications

Grasping virtual fish: A step towards robotic deep learning from demonstration in virtual reality

Grasping virtual fish: A step towards robotic deep learning from demonstration in virtual reality

Teaching a Robot to Grasp Real Fish by Imitation Learning from a Human Supervisor in Virtual Reality

Autonomous Robots and Behavior Initiators

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