Eleftherios Triantafyllidis

Sep 7, 2023 | nature.com | Eleftherios Triantafyllidis

AbstractSolving long sequential tasks remains a non-trivial challenge in the field of embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is a notable open problem and continues to be an active area of research.

Aug 21, 2023 | nature.com | Wanming Yu |Auke Ijspeert |Christopher McGreavy |Eleftherios Triantafyllidis |Guillaume Bellegarda

AbstractRobot motor skills can be acquired by deep reinforcement learning as neural networks to reflect state–action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neural networks, human insights and engineering efforts are often required to select appropriate states through qualitative approaches, such as ablation studies, without a quantitative analysis of the state importance.