面向过约束运动的大规模多地形深度强化学习驱动的进化形态学
Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning
AI总结：
本文受进化形态学启发，提出可重构为平面、Bennett和球面四杆机构的3D打印机器人肢体，并通过大规模多地形深度强化学习证明过约束肢体在多种地形下比平面肢体至少节能22%、平地速度提升20%。
摘要：
虽然动物的鳍到肢进化已在生物学中得到深入研究，但这种形态转变在现代先进机器人肢体的设计中仍未得到充分采用。本文从设计和学习的视角研究一类新颖的过约束运动，其灵感来自进化形态学，旨在将“约束下的智能设计”——下文称为约束驱动的设计智能——这一概念融入开发具有卓越能量效率的现代机器人肢体中。我们提出了一种可3D打印的机器人肢体设计，该肢体可参数化重构为经典平面四杆机构、过约束Bennett连杆机构和球面四杆机构。这些肢体采用与现代腿足机器人平台相同的同轴驱动方式，并具备升级为轮腿系统的附加能力。然后，我们实现了一个大规模、多地形的深度强化学习框架，用于训练这些可重构肢体，以对过约束运动在能量效率方面进行比较分析。结果表明，在不同地形（包括平地、斜坡和楼梯）上，无论是否存在随机噪声，过约束肢体在前向和侧向行走过程中都比平面肢体表现出更高效的运动，完成穿越任务时至少节省22%的机械能，其中球面肢体效率最低。该方案还在平坦地形上实现了0.85米/秒的最高平均速度，比平面肢体快20%。这项研究为未来过约束机器人学中利用进化形态学和可重构机构智能，并结合最先进的深度强化学习方法，开辟了一个令人兴奋的研究方向。
英文摘要：
While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological transformation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of `intelligent design under constraints' - hereafter referred to as constraint-driven design intelligence - in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4-bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85 meters per second on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning.
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