[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Kegeleirs et al., 2024 - Google Patents

Transferability in the automatic off-line design of robot swarms: from sim-to-real to embodiment and design-method transfer across different platforms

Kegeleirs et al., 2024

View PDF
Document ID
12971000977828609733
Author
Kegeleirs M
Ramos D
Hasselmann K
Garattoni L
Francesca G
Birattari M
Publication year
Publication venue
IEEE Robotics and Automation Letters

External Links

Snippet

Automatic off-line design is an attractive approach to implementing robot swarms. In this approach, a designer specifies a mission to be accomplished by the swarm, and an optimization process generates suitable control software for the individual robots through …
Continue reading at drive.google.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/004—Artificial life, i.e. computers simulating life
    • G06N3/008—Artificial life, i.e. computers simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. robots replicating pets or humans in their appearance or behavior
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems

Similar Documents

Publication Publication Date Title
Dasari et al. Transformers for one-shot visual imitation
Silva et al. Open issues in evolutionary robotics
Chatzilygeroudis et al. A survey on policy search algorithms for learning robot controllers in a handful of trials
Yahya et al. Collective robot reinforcement learning with distributed asynchronous guided policy search
Francesca et al. AutoMoDe: A novel approach to the automatic design of control software for robot swarms
Calinon et al. Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning
Risi et al. Indirectly encoding neural plasticity as a pattern of local rules
Risi et al. A unified approach to evolving plasticity and neural geometry
Kim et al. From exploration to control: learning object manipulation skills through novelty search and local adaptation
Nguyen et al. Socially guided intrinsic motivation for robot learning of motor skills
Kuckling et al. Behavior trees as a control architecture in the automatic modular design of robot swarms
Paine et al. Motor primitive and sequence self-organization in a hierarchical recurrent neural network
Fang et al. Dynamics learning with cascaded variational inference for multi-step manipulation
Francesca et al. An experiment in automatic design of robot swarms: AutoMoDe-Vanilla, EvoStick, and human experts
Kegeleirs et al. Transferability in the automatic off-line design of robot swarms: from sim-to-real to embodiment and design-method transfer across different platforms
Fox et al. Multi-task hierarchical imitation learning for home automation
Doncieux et al. Evolutionary robotics: Exploring new horizons
Birattari et al. AutoMoDe: a modular approach to the automatic off-line design and fine-tuning of control software for robot swarms
Keymeulen et al. Online evolution for a self-adapting robotic navigation system using evolvable hardware
Persiani et al. A working memory model improves cognitive control in agents and robots
Jiang et al. A novel simulation-reality closed-loop learning framework for autonomous robot skill learning
Mertan et al. Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots
Enayati et al. A methodical interpretation of adaptive robotics: Study and reformulation
Li et al. Evaluation of frameworks that combine evolution and learning to design robots in complex morphological spaces
Wellmer et al. Dropout’s dream land: Generalization from learned simulators to reality