Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
On the Gibbs Phenomenon and Its Resolution
SIAM Review
Synthesizing animations of human manipulation tasks
ACM SIGGRAPH 2004 Papers
Incremental Online Learning in High Dimensions
Neural Computation
Neurocomputing
Animation planning for virtual characters cooperation
ACM SIGGRAPH 2008 classes
HAID '08 Proceedings of the 3rd international workshop on Haptic and Audio Interaction Design
Vibrotactile perception assessment for a rowing training system
WHC '09 Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems
Robotics and Autonomous Systems
Physically valid statistical models for human motion generation
ACM Transactions on Graphics (TOG)
A Sinusoidal Family of Unitary Transforms
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In the present work, we describe a mathematical model to generate human-like motion trajectories in space. We use linear regression in a latent space to find the model parameters from a set of demonstration examples. The learning procedure requires a relevant set of similar examples. The apprehended models encode both the typical shapes of motion and their variability towards specific boundary conditions (BC). We will show the added value of encoding both properties in a unique model and we apply this ability to common problems of error compensation and target tracking. The models allow us to describe human motion using expansion-function series (EFS), thus avoiding typical stability issues that arise in the use of differential equation models. To cope with variable scenarios, we show two specific algorithms that morph and adapt the evolution trajectory. In analogy to splines, the EFS preserve an analytical structure on which we develop the optimisation steps. In such a way, we managed to combine multiple single segments into complex motions that preserve continuity and may simultaneously optimise other criteria. In the present work, after having analysed similar tools, we present the basic model and its features. Then we develop a robust tool to gather the model from examples, and to achieve real-time trajectory adaptation. The achieved results will be analysed through an experimental analysis on data collected in a ball catching experiment.