Finite State Machine Policies Modulating Trajectory Generator,arXiv |
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Finite State Machine Policies Modulating Trajectory Generator,arXiv
|
Deep reinforcement learning (deep RL) has emerged as an effective tool for
developing controllers for legged robots. However, a simple neural network
representation is known for its poor extrapolation ability, making the learned
behavior vulnerable to unseen perturbations or challenging terrains. Therefore,
researchers have investigated a novel architecture, Policies Modulating
Trajectory Generators (PMTG), which combines trajectory generators (TG) and
feedback control signals to achieve more robust behaviors. In this work, we
propose to extend the PMTG framework with a finite state machine PMTG by
replacing simple TGs with asynchronous finite state machines (Async FSMs). This
invention offers an explicit notion of contact events to the policy to
negotiate unexpected perturbations. We demonstrated that the proposed
architecture could achieve more robust behaviors in various scenarios, such as
challenging terrains or external perturbations, on both simulated and real
robots. The supplemental video can be found at: http://youtu.be/XUiTSZaM8f0.
中文翻译：
深度强化学习 (deep RL) 已成为开发腿式机器人控制器的有效工具。然而，简单的神经网络表示以其较差的外推能力而闻名，这使得学习行为容易受到看不见的扰动或具有挑战性的地形的影响。因此，研究人员研究了一种新颖的架构，即策略调制轨迹生成器 (PMTG)，它结合了轨迹生成器 (TG) 和反馈控制信号以实现更稳健的行为。在这项工作中，我们建议通过用异步有限状态机 (Async FSM) 替换简单的 TG 来扩展具有有限状态机 PMTG 的 PMTG 框架。本发明为协商意外扰动的策略提供了明确的接触事件概念。我们证明了所提出的架构可以在模拟和真实机器人上的各种场景中实现更稳健的行为，例如具有挑战性的地形或外部扰动。补充视频可在以下网址找到：http://youtu.be/XUiTSZaM8f0。 |
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