RAS 2010 - PDF Preprint Papers

RAS 2010

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RAS
2010

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Policy gradient learning for quadruped soccer robots

12 years 11 months ago

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Andrea Cherubini, Francesca Giannone, Luca Iocchi,...

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Id	Views	Title	Status

1	220	Policy gradient learning for quadruped soccer robots	claim
2	216	6D scan registration using depth-interpolated local image features	claim
3	216	A nonparametric learning approach to range sensing from omnidirectional vision	claim
4	167	Data association and occlusion handling for vision-based people tracking by mobile robots	claim
5	164	Bridging the gap between feature- and grid-based SLAM	claim
6	164	Towards performing everyday manipulation activities	claim
7	142	Closing the loop in appearance-guided omnidirectional visual odometry by using vocabulary trees	claim
8	137	Development of complex robotic systems using the behavior-based control architecture iB2C	claim
9	136	A realistic benchmark for visual indoor place recognition	claim
10	131	Probabilistic Policy Reuse for inter-task transfer learning	claim
11	127	Maximum-likelihood sample-based maps for mobile robots	claim
12	123	An information-based exploration strategy for environment mapping with mobile robots	claim
13	117	Direct programming of a central pattern generator for periodic motions by touching	claim
14	117	Extending BDI plan selection to incorporate learning from experience	claim
15	115	Learning grasping points with shape context	claim
16	109	Combining active learning and reactive control for robot grasping	claim
17	108	Swarm-supported outdoor localization with sparse visual data	claim
18	108	Experimental validation of collective circular motion for nonholonomic multi-vehicle systems	claim
19	106	A developmental algorithm for ocular-motor coordination	claim
20	104	A hybrid controller based on the egocentric perceptual principle	claim
21	97	Repeated auctions for robust task execution by a robot team	claim
22	84	Robust formation control and servoing using switching range sensors	claim
23	73	Image similarity based on Discrete Wavelet Transform for robots with low-computational resources	claim

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