A robot ping-pong player: experiment in real-time intelligent control
A robot ping-pong player: experiment in real-time intelligent control
Modeling and prediction of human behavior
Neural Computation
Learning nonlinear dynamical systems using an EM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Learning and inferring transportation routines
Artificial Intelligence
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intentional motion on-line learning and prediction
Machine Vision and Applications
Analytic moment-based Gaussian process filtering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
International Journal of Robotics Research
Planning-based prediction for pedestrians
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Motion planning under uncertainty for robotic tasks with long time horizons
International Journal of Robotics Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Journal of Real-Time Image Processing
Probabilistic pointing target prediction via inverse optimal control
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Learning Approach to Robotic Table Tennis
IEEE Transactions on Robotics
Learning to select and generalize striking movements in robot table tennis
International Journal of Robotics Research
| Hi-index | 0.00 |
Intention inference can be an essential step toward efficient human-robot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows the intention to be inferred from observed movements using Bayes' theorem. The IDDM simultaneously finds a latent state representation of noisy and high-dimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e. target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.