GLAMDRING：通过CPG的强化学习实现步态学习与形态协同设计

中文摘要 AI 辅助

机器人正从结构化工厂车间走向非结构化环境，如灾难现场、行星表面和农业领域，而针对这些环境，合适的机器人往往尚不存在。我们提出GLAMDRING，一个为 locomotion 任务合成最优机器人并同时学习驱动其控制器的框架。针对给定的前进速度范围、每个执行器的功率预算、执行器库和有效载荷要求，GLAMDRING 返回匹配的四足形态（连杆几何和每个关节的执行器）以及 Hopf 振荡器中枢模式生成器（CPG）步态策略。我们根据目标设计目标（即最大速度、最小运输成本（CoT）或最大有效载荷裕度）对可行设计进行排序。由于身体和运动是耦合的，最优形态决定了机器人如何被驱动，而最优步态则依赖于物理身体。我们通过强化学习在候选形态空间中训练少量 CPG 策略，将步态与底层机器人硬件共同学习。连杆长度和执行器随后从策略记录的运行包络中事后确定，将合成成本降低到少量固定的强化学习运行次数，而不是每个候选一次。我们的实验展示了三个关键发现：身体和步态的协同设计对于满足运动约束是必要的；执行器包络可行性（而非仅运动成功）决定了可实现的有效载荷能力；以及典型的动物步态在大多数设计中仅从形态和约束中自然涌现。真实世界演示进一步突出了我们工作的有效性。

英文摘要

Robots are moving out of the structured factory floor and into unstructured environments such as disaster sites, planetary surfaces, and agricultural fields, for which the right robot often does not yet exist. We present GLAMDRING, a framework that synthesizes the optimal robot for a locomotion task and, jointly, learns the controller that drives it. For the given specifications of forward-velocity bounds, a per-actuator power budget, an actuator library, and a payload requirement, GLAMDRING returns a matched quadruped morphology (link geometry and per-joint actuators) and a Hopf-oscillator Central Pattern Generator (CPG) gait policy. We rank feasible designs against a target design objective, viz., maximum speed, minimum Cost of Transport (CoT), or max Payload Margin. Because body and locomotion are coupled, the optimal morphology dictates how a robot is driven, while optimal gait depends on the physical body. We train a small number of CPG policies by reinforcement learning across the space of candidate morphologies, co-learning the gait with the underlying robot hardware. Link lengths and actuators are then resolved post-hoc from the policy's logged operating envelope, reducing synthesis cost to a small, fixed number of reinforcement-learning runs instead of one per candidate. Our experiments show three key findings: co-designing body and gait is necessary to satisfy locomotion constraints; actuator-envelope feasibility, rather than locomotion success alone, determines realizable payload capacity; and canonical animal gaits emerge naturally in most designs from morphology and constraints alone. A real-world demonstration further highlights the efficacy of our work.

发表机构

• Purdue University（普渡大学）

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