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Lemme, Andre: Bootstrapping movement primitives from complex trajectories. 2014
Inhalt
Introduction
Background and Related Work
The idea of motion primitives from a biological perspective
Motion primitive representation and learning in robotics
Architectural perspective on motion skill learning
Movement Primitives Imprinted in Neural Networks
Learning vector field representations from sparse data
Neurally imprinted vector fields for motion generation
Implementation of asymptotic stability
Design of data-driven Lyapunov candidates
Impact of stability-constraints on the resulting dynamical systems
Learning from kinesthetic teaching
Discussion
Concluding remarks
Comparisons of Movements under Benchmark Conditions
Design of a systematic comparison
Benchmark system for human-like motion generation methods
A dataset of human handwriting demonstrations
Benchmark scenarios
Evaluation and performance measures
Statistical analysis of benchmark results
Comparison with focus on NiVF
Conclusion
Adaptive Sequencing of Movement Primitives with Neural Networks
Generation of complex movements from movement primitives
Holistic neural learning architecture
Learning methodology
Composition of complex trajectories
Discussion and perspectives
Conclusion
Creation and Refinement of Movement Primitives by Co-Articulation
Notion of co-articulation
Noise-induced learning process for Dynamic Movement Primitives
Co-articulation in a via-point task
Discussion
Conclusion
Perceiving known Movement Primitives in Complex Movements
Using the agent's own movement capabilities for perception
Methodology for decomposing and interpreting trajectories
Decomposing complex trajectories with a movement primitive library
Discussion
Conclusion
Bootstrapping of Movement Primitives
Learning cycle of movement primitives
Semi-supervised bootstrapping of movement primitives
Learning process of the bootstrapping cycle from 2D trajectories
Robotic application
Concluding remarks
Conclusion
Appendix
References