most citedMap-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning

10 citations · 10 across the 2 of their papers we have counts for

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cs.RO2019

Motion Generation Considering Situation with Conditional Generative Adversarial Networks for Throwing Robots

Kyo Kutsuzawa, Hitoshi Kusano, Ayaka Kume +1

When robots work in a cluttered environment, the constraints for motions change frequently and the required action can change even for the same task. However, planning complex moti…

cs.RO2017★ 10 cited

Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning

Ayaka Kume, Eiichi Matsumoto, Kuniyuki Takahashi +2

In order for robots to perform mission-critical tasks, it is essential that they are able to quickly adapt to changes in their environment as well as to injuries and or other bodil…