Distributed goal recognition algorithms for modular robots

2003 ◽  
Author(s):  
Z. Butler ◽  
R. Fitch ◽  
D. Rus ◽  
Yuhang Wang
Keyword(s):  
Modular Robots ◽  
Recognition Algorithms ◽  
Goal Recognition

New Goal Recognition Algorithms Using Attack Graphs

2019 ◽  
pp. 260-278
Author(s):  
Reuth Mirsky ◽  
Ya’ar Shalom ◽  
Ahmad Majadly ◽  
Kobi Gal ◽  
Rami Puzis ◽  
...  
Keyword(s):  
Attack Graphs ◽  
Recognition Algorithms ◽  
Goal Recognition

scholarly journals Advances in Pattern Recognition Algorithms, Architectures, and Devices

2004 ◽  
Vol 43 (8) ◽  
pp. 1702 ◽  
Author(s):  
Mohammad A. Karim
Keyword(s):  
Pattern Recognition ◽  
Recognition Algorithms

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

Expecting the unexpected: Goal recognition for rational and irrational agents

2021 ◽  
Vol 297 ◽  
pp. 103490
Author(s):  
Peta Masters ◽  
Sebastian Sardina
Keyword(s):  
Goal Recognition

scholarly journals A reconfiguration strategy for modular robots using origami folding

2018 ◽  
Vol 38 (1) ◽  
pp. 73-89 ◽  
Author(s):  
Meibao Yao ◽  
Christoph H. Belke ◽  
Hutao Cui ◽  
Jamie Paik
Keyword(s):  
Heuristic Algorithm ◽  
Minimum Energy ◽  
Computational Time ◽  
Modular Robots ◽  
Automatic Modeling ◽  
Minimum Energy Consumption ◽  
Modular Units ◽  
Optimal Reconfiguration ◽  
Algorithmic Framework ◽  
Modeling Algorithm

Reconfigurability in versatile systems of modular robots is achieved by changing the morphology of the overall structure as well as by connecting and disconnecting modules. Recurrent connectivity changes can cause misalignment that leads to mechanical failure of the system. This paper presents a new approach to reconfiguration, inspired by the art of origami, that eliminates connectivity changes during transformation. Our method consists of an energy-optimal reconfiguration planner that generates an initial 2D assembly pattern and an actuation sequence of the modular units, both resulting in minimum energy consumption. The algorithmic framework includes two approaches, an automatic modeling algorithm as well as a heuristic algorithm. We further demonstrate the effectiveness of our method by applying the algorithms to Mori, a modular origami robot, in simulation. Our results show that the heuristic algorithm yields reconfiguration schemes with high quality, compared with the automatic modeling algorithm, simultaneously saving a considerable amount of computational time and effort.

scholarly journals Current trends in reconfigurable modular robots design

2017 ◽  
Vol 14 (3) ◽  
pp. 172988141771045 ◽  
Author(s):  
Alberto Brunete ◽  
Avinash Ranganath ◽  
Sergio Segovia ◽  
Javier Perez de Frutos ◽  
Miguel Hernando ◽  
...  
Keyword(s):  
Modular Robots ◽  
Current Trends

Large-vocabulary speech recognition algorithms

Computer ◽  
2002 ◽  
Vol 35 (3) ◽  
pp. 42-50 ◽  
Author(s):  
M. Padmanabhan ◽  
M. Picheny
Keyword(s):  
Speech Recognition ◽  
Large Vocabulary ◽  
Recognition Algorithms ◽  
Large Vocabulary Speech Recognition
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