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Multi-Fingered Active Grasp Learning
[article]
2020
arXiv
pre-print
Learning-based approaches to grasp planning are preferred over analytical methods due to their ability to better generalize to new, partially observed objects. However, data collection remains one of the biggest bottlenecks for grasp learning methods, particularly for multi-fingered hands. The relatively high dimensional configuration space of the hands coupled with the diversity of objects common in daily life requires a significant number of samples to produce robust and confident grasp
arXiv:2006.05264v2
fatcat:wa4a2qmxyjhj7budqwxbyl3qbq