Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter

2021 ◽  
Author(s):  
Bardienus P. Duisterhof ◽  
Srivatsan Krishnan ◽  
Jonathan J. Cruz ◽  
Colby R. Banbury ◽  
William Fu ◽  
...  
Keyword(s):  
Robot Learning ◽  
Source Seeking

scholarly journals Limited Gradient Criterion for Global Source Seeking with Mobile Robots

2020 ◽  
Vol 53 (2) ◽  
pp. 15288-15293
Author(s):  
Marcus Gronemeyer ◽  
Mirco Alpen ◽  
Joachim Horn
Keyword(s):  
Mobile Robots ◽  
Source Seeking ◽  
Global Source

scholarly journals Acceleration-Actuated Source Seeking Without Position and Velocity Sensing

2020 ◽  
Vol 53 (2) ◽  
pp. 5348-5355
Author(s):  
Raik Suttner ◽  
Miroslav Krstić
Keyword(s):  
Source Seeking

scholarly journals Generalization-Based Acquisition of Training Data for Motor Primitive Learning by Neural Networks

2021 ◽  
Vol 11 (3) ◽  
pp. 1013
Author(s):  
Zvezdan Lončarević ◽  
Rok Pahič ◽  
Aleš Ude ◽  
Andrej Gams
Keyword(s):  
Neural Networks ◽  
Dimensionality Reduction ◽  
Gaussian Process Regression ◽  
Search Space ◽  
Robot Learning ◽  
Training Data ◽  
Practical Applications ◽  
Latent Space ◽  
Real Robot ◽  
Low Dimensional

Autonomous robot learning in unstructured environments often faces the problem that the dimensionality of the search space is too large for practical applications. Dimensionality reduction techniques have been developed to address this problem and describe motor skills in low-dimensional latent spaces. Most of these techniques require the availability of a sufficiently large database of example task executions to compute the latent space. However, the generation of many example task executions on a real robot is tedious, and prone to errors and equipment failures. The main result of this paper is a new approach for efficient database gathering by performing a small number of task executions with a real robot and applying statistical generalization, e.g., Gaussian process regression, to generate more data. We have shown in our experiments that the data generated this way can be used for dimensionality reduction with autoencoder neural networks. The resulting latent spaces can be exploited to implement robot learning more efficiently. The proposed approach has been evaluated on the problem of robotic throwing at a target. Simulation and real-world results with a humanoid robot TALOS are provided. They confirm the effectiveness of generalization-based database acquisition and the efficiency of learning in a low-dimensional latent space.

Recent Advances on Vision-Based Robot Learning by Demonstration

2018 ◽  
Vol 11 (4) ◽  
pp. 269-284
Author(s):  
Hongxin Zhang ◽  
Xingyu Lv ◽  
Wancong Leng ◽  
Xuefeng Ma
Keyword(s):  
Robot Learning ◽  
Learning By Demonstration ◽  
Recent Advances
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