Kernel principal component analysis
Advances in kernel methods
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Style translation for human motion
ACM SIGGRAPH 2005 Papers
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Multifactor Gaussian process models for style-content separation
Proceedings of the 24th international conference on Machine learning
Factored conditional restricted Boltzmann Machines for modeling motion style
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Task-specific generalization of discrete and periodic dynamic movement primitives
IEEE Transactions on Robotics
Interaction learning for dynamic movement primitives used in cooperative robotic tasks
Robotics and Autonomous Systems
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Learning from demonstration has shown to be a suitable approach for learning control policies (CPs). However, most previous studies learn CPs from a single demonstration, which results in limited scalability and insufficient generalization toward a wide range of applications in real environments. This paper proposes a novel approach to learn highly scalable CPs of basis movement skills from multiple demonstrations. In contrast to conventional studies with a single demonstration, i.e., dynamic movement primitives (DMPs), our approach efficiently encodes multiple demonstrations by shaping a parametric-attractor landscape in a set of differential equations. Assuming a certain similarity among multiple demonstrations, our approach learns the parametric-attractor landscape by extracting a small number of common factors in multiple demonstrations. The learned CPs allow the synthesis of novel movements with novel motion styles by specifying the linear coefficients of the bases as parameter vectors without losing useful properties of the DMPs, such as stability and robustness against perturbations. For both discrete and rhythmic movement skills, we present a unified learning procedure for learning a parametric-attractor landscape from multiple demonstrations. The feasibility and highly extended scalability of DMPs are demonstrated on an actual dual-arm robot.