Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Shaping and policy search in reinforcement learning
Shaping and policy search in reinforcement learning
Continuation methods for adapting simulated skills
ACM SIGGRAPH 2008 papers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning complex motions by sequencing simpler motion templates
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The development of hierarchical knowledge in robot systems
The development of hierarchical knowledge in robot systems
Abstraction and generalization in reinforcement learning: a summary and framework
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
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Humans and animals acquire their wide repertoire of motor skills through an incremental learning process, during which progressively more complex skills are acquired and subsequently integrated with prior abilities. Inspired by this general idea, we develop an approach for learning motor skills based on a two-level curriculum. At the high level, the curriculum specifies an order in which different skills should be learned. At the low level, the curriculum defines a process for learning within a skill. We develop a set of integrated motor skills for a planar articulated figure capable of doing parameterized hops, flips, rolls, and acrobatic sequences. The same curriculum can be applied to yield individualized motor skill sets for articulated figures of varying proportions.