document: Scalable techniques from nonparametric statistics for real time robot learning Locally weighted learning -LRB- LWL -RRB- is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems . The paper introduces several LWL algorithms that have been tested successfully in real-time learning of complex robot tasks . We discuss two major classes of LWL , memory-based LWL and purely incremental LWL that does not need to remember any data explicitly . In contrast to the traditional belief that LWL methods can not work well in high-dimensional spaces , we provide new algorithms that have been tested on up to 90 dimensional learning problems . The applicability of our LWL algorithms is demonstrated in various robot learning examples , including the learning of devil-sticking , pole-balancing by a humanoid robot arm , and inverse-dynamics learning for a seven and a 30 degree-of-freedom robot . In all these examples , the application of our statistical neural networks techniques allowed either faster or more accurate acquisition of motor control than classical control engineering
doc_bio_tags: B I O B I O B I I I B I I O O O O O O O O O B I O O O O O B I O O O B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O
extractive_keyphrases: scalable techniques nonparametric statistics real time robot learning locally weighted learning training algorithms complex phenomena autonomous adaptive control humanoid robot arm statistical neural networks techniques
abstractive_keyphrases: memory-based learning purely incremental learning devil-sticking pole-balancing inverse-dynamics learning nonparametric regression