Sep 14, 2022 · We propose a concurrent network framework that combines online and offline reinforcement learning (RL) methods.
In this paper, we focus on co-design unchanging topology problems, propose a novel framework, and construct a four-legged robot to perform hardware validation.
With the rise of computing power, using data-driven approaches for co-designing robots' morphology and controller has become a feasible way.
Missing: C2: | Show results with:C2:
It also showcased that using a fixed control policy, solely morphological updates could enable a robot to step over a 10 cm platform.
Missing: C2: | Show results with:C2:
TL;DR: This work proposes a concurrent network framework that combines online and offline reinforcement learning (RL) methods that effectively addresses issues ...
Oct 21, 2024 · Enhancement of Reinforcement Learning Algorithm through Design of Deep Learning Networks for Jumping Robot. Kim, Hyeonjin, Seoul National ...
C: Co-design of Robots via Concurrent-Network Coupling Online and Offline Reinforcement Learning. C Chen, P Xiang, H Lu, Y Wang, R Xiong. 2023 IEEE/RSJ ...
Missing: C2: | Show results with:C2:
People also ask
Do robots use reinforcement learning?
What are the benefits of robots and Cobots?
C^2: Co-Design of Robots Via Concurrent-Network Coupling Online and Offline Reinforcement Learning; CaRE: Finding Root Causes of Configuration Issues in ...
We find that history-dependent models can be extremely effective in learning from single and multi-human datasets while state-of-the-art batch RL algorithms ...
Jul 14, 2023 · To this end, we proposed a co-optimization framework based on hierarchical Deep Reinforcement Learning (DRL), consisting of a configuration ...
Missing: C2: Offline