Computational geometry: an introduction
Computational geometry: an introduction
Model-based control of a robot manipulator
Model-based control of a robot manipulator
Optimal control: linear quadratic methods
Optimal control: linear quadratic methods
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Efficient Locally Weighted Polynomial Regression Predictions
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploration and apprenticeship learning in reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Using inaccurate models in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Analysis of sibling time series data: alignment and difference detection
Analysis of sibling time series data: alignment and difference detection
Learning for control from multiple demonstrations
Proceedings of the 25th international conference on Machine learning
Bayesian inverse reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Maneuver-based motion planning for nonlinear systems with symmetries
IEEE Transactions on Robotics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Situated learning of visual robot behaviors
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems
Journal of Field Robotics
Adaptive fast open-loop maneuvers for quadrocopters
Autonomous Robots
Learning the combinatorial structure of demonstrated behaviors with inverse feedback control
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Apprenticeship learning with few examples
Neurocomputing
Safe exploration of state and action spaces in reinforcement learning
Journal of Artificial Intelligence Research
Provably safe and robust learning-based model predictive control
Automatica (Journal of IFAC)
Data-driven control of flapping flight
ACM Transactions on Graphics (TOG)
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Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the edge of the helicopterâ聙聶s capabilities. We present apprenticeship learning algorithms, which leverage expert demonstrations to efficiently learn good controllers for tasks being demonstrated by an expert. These apprenticeship learning algorithms have enabled us to significantly extend the state of the art in autonomous helicopter aerobatics. Our experimental results include the first autonomous execution of a wide range of maneuvers, including but not limited to in-place flips, in-place rolls, loops and hurricanes, and even auto-rotation landings, chaos and tic-tocs, which only exceptional human pilots can perform. Our results also include complete airshows, which require autonomous transitions between many of these maneuvers. Our controllers perform as well as, and often even better than, our expert pilot.