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Using Tactile Sensing to Improve the Sample Efficiency and Performance of Deep Deterministic Policy Gradients for Simulated In-Hand Manipulation Tasks
Deep Reinforcement Learning techniques demonstrate advances in the domain of robotics. One of the limiting factors is a large number of interaction samples usually required for training in simulated and real-world environments. In this work, we demonstrate for a set of simulated dexterous in-hand ob...
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| Pubblicato in: | Front Robot AI |
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| Autori principali: | , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Frontiers Media S.A.
2021
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8275974/ https://ncbi.nlm.nih.gov/pubmed/34268337 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/frobt.2021.538773 |
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