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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Energy-efficient and damage-recovery slithering gait design for a snake-like robot based on reinforcement learning and inverse reinforcement learning
ترجمه فارسی عنوان مقاله:
طراحی راه رفتن لغزنده با صرفه جویی در مصرف انرژی و آسیب دیدگی برای یک ربات مار مانند بر اساس یادگیری تقویتی و یادگیری تقویتی معکوس
منبع:
Sciencedirect - Elsevier - Neural Networks, 129 (2020) 323-333. doi:10.1016/j.neunet.2020.05.029
نویسنده:
Zhenshan Bing a, Christian Lemke b, Long Cheng c,∗, Kai Huang d, Alois Knoll a
چکیده انگلیسی:
Similar to real snakes in nature, the flexible trunks of snake-like robots enhance their movement
capabilities and adaptabilities in diverse environments. However, this flexibility corresponds to a
complex control task involving highly redundant degrees of freedom, where traditional modelbased
methods usually fail to propel the robots energy-efficiently and adaptively to unforeseeable
joint damage. In this work, we present an approach for designing an energy-efficient and damagerecovery
slithering gait for a snake-like robot using the reinforcement learning (RL) algorithm and the
inverse reinforcement learning (IRL) algorithm. Specifically, we first present an RL-based controller for
generating locomotion gaits at a wide range of velocities, which is trained using the proximal policy
optimization (PPO) algorithm. Then, by taking the RL-based controller as an expert and collecting
trajectories from it, we train an IRL-based controller using the adversarial inverse reinforcement
learning (AIRL) algorithm. For the purpose of comparison, a traditional parameterized gait controller
is presented as the baseline and the parameter sets are optimized using the grid search and Bayesian
optimization algorithm. Based on the analysis of the simulation results, we first demonstrate that
this RL-based controller exhibits very natural and adaptive movements, which are also substantially
more energy-efficient than the gaits generated by the parameterized controller. We then demonstrate
that the IRL-based controller cannot only exhibit similar performances as the RL-based controller, but
can also recover from the unpredictable damage body joints and still outperform the model-based
controller, which has an undamaged body, in terms of energy efficiency.Videos can be viewed at
https://videoviewsite.wixsite.com/rlsnake.
Keywords: Snake-like robot | Reinforcement learning | Inverse reinforcement learning | Motion planning | Damage recovery
قیمت: رایگان
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