Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Dynamically-Stable Motion Planning for Humanoid Robots
Autonomous Robots
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces
ACM SIGGRAPH 2004 Papers
Intrinsic dimensionality estimation of submanifolds in Rd
ICML '05 Proceedings of the 22nd international conference on Machine learning
Incremental Online Learning in High Dimensions
Neural Computation
Learning Nonlinear Image Manifolds by Global Alignment of Local Linear Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Springer Handbook of Robotics
Planning Algorithms
Non-isometric manifold learning: analysis and an algorithm
Proceedings of the 24th international conference on Machine learning
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Flow-through policies for hybrid controller synthesis applied to fully actuated systems
IEEE Transactions on Robotics
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Manipulation planning on constraint manifolds
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Global vector field computation for feedback motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Robotics: Science and Systems III
Robotics: Science and Systems III
LQR-trees: Feedback Motion Planning via Sums-of-Squares Verification
International Journal of Robotics Research
Learning Non-linear Multivariate Dynamics of Motion in Robotic Manipulators
International Journal of Robotics Research
Maneuver-based motion planning for nonlinear systems with symmetries
IEEE Transactions on Robotics
Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations
IEEE Transactions on Robotics
Discrete Geometric Optimal Control on Lie Groups
IEEE Transactions on Robotics
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We address the problem of encoding and executing skills, i.e. motion tasks involving a combination of specifications regarding constraints and variability. We take an approach that is model-free in the sense that we do not assume an explicit and complete analytical specification of the task - which can be hard to obtain for many realistic robot systems. Instead, we learn an encoding of the skill from observations of an initial set of sample trajectories. This is achieved by encoding trajectories in a skill manifold which is learnt from data and generalizes in the sense that all trajectories on the manifold satisfy the constraints and allowable variability in the demonstrated samples. In new instances of the trajectory-generation problem, we restrict attention to geodesic trajectories on the learnt skill manifold, making computation more tractable. This procedure is also extended to accommodate dynamic obstacles and constraints, and to dynamically react against unexpected perturbations, enabling a form of model-free feedback control with respect to an incompletely modelled skill. We present experiments to validate this framework using various robotic systems - ranging from a three-link arm to a small humanoid robot - demonstrating significant computational improvements without loss of accuracy. Finally, we present a comparative evaluation of our framework against a state-of-the-art imitation-learning method.