Robot-to-human handover with obstacle avoidance via continuous time Recurrent Neural Network

2016 ◽  
Author(s):  
Huajin Tang ◽  
Boon Hwa Tan ◽  
Rui Yan
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Recurrent Neural Network ◽  
Continuous Time

scholarly journals Active Shape Discrimination with Compliant Bodies as Reservoir Computers

2016 ◽  
Vol 22 (2) ◽  
pp. 241-268 ◽  
Author(s):  
Chris Johnson ◽  
Andrew Philippides ◽  
Philip Husbands
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Continuous Time ◽  
Complex Dynamics ◽  
The Body ◽  
Control Problems ◽  
Reservoir Computing ◽  
Shape Discrimination ◽  
Reflexive Behavior ◽  
Processing Information

Compliant bodies with complex dynamics can be used both to simplify control problems and to lead to adaptive reflexive behavior when engaged with the environment in the sensorimotor loop. By revisiting an experiment introduced by Beer and replacing the continuous-time recurrent neural network therein with reservoir computing networks abstracted from compliant bodies, we demonstrate that adaptive behavior can be produced by an agent in which the body is the main computational locus. We show that bodies with complex dynamics are capable of integrating, storing, and processing information in meaningful and useful ways, and furthermore that with the addition of the simplest of nervous systems such bodies can generate behavior that could equally be described as reflexive or minimally cognitive.

scholarly journals Evolution of Situated and Abstract Communication in Leader Selection and Borderline Identification Swarm Robotics Problems

2021 ◽  
Vol 11 (8) ◽  
pp. 3516
Author(s):  
Rafael Sendra-Arranz ◽  
Álvaro Gutiérrez
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Continuous Time ◽  
Swarm Robotics ◽  
Direct Interaction ◽  
Identification Problem ◽  
Selection Task ◽  
Natural Evolution ◽  
Starting Conditions ◽  
Leader Selection

The design of robust yet simple communication mechanisms, that allow the cooperation through direct interaction among robots, is an important aspect of swarm robotics systems. In this paper, we analyze how an identical continuous-time recurrent neural network (CTRNN) controller can lead to the emergence of different kinds of communications within the swarm, either abstract or situated, depending on the problem to be faced. More precisely, we address two swarm robotics tasks that require, at some extent, communication to be solved: leader selection and borderline identification. The parameters of the CTRNN are evolved using separable natural evolution strategies. It is shown that, using the same starting conditions and robots’ controllers, the evolution process leads to the emergence of utterly diverging communications. Firstly, an abstract communication, in which the message carries all the information, results from evolution in the leader selection task. Alternatively, a purely situated communication, meaning that only the context is communicative, emerges when dealing with the borderline identification problem. Nonetheless, scalability and robustness properties are successfully validated.

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