学习多样自然行为以增强四足机器人的敏捷性
Learning Diverse Natural Behaviors for Enhancing the Agility of Quadrupedal Robots
AI总结：
提出由BBC与TSC组成的集成控制器，结合半监督GAIL、特权学习和进化对抗模拟器辨识，使四足机器人学会并迁移多样自然行为，完成平均1.1 m/s、跨栏峰值3.2 m/s的敏捷挑战。
摘要：
实现类似动物的敏捷性一直是四足机器人领域的长期目标。尽管近期研究已成功展示了对特定行为的模仿，但让机器人在真实环境中复现更广泛的自然行为仍是一项开放挑战。为此，我们提出一种由基础行为控制器（Basic Behavior Controller，BBC）和任务专用控制器（Task-Specific Controller，TSC）组成的集成控制器，能够在增强型模拟器中有效学习多样的四足自然行为，并将其高效迁移到真实世界。具体而言，BBC采用一种新型半监督生成对抗模仿学习算法进行训练，从真实犬只的原始动作捕捉数据中提取多样行为风格，并通过调节离散和连续潜变量输入实现平滑的行为转换。TSC通过特权学习进行训练，以深度图像作为输入，协调BBC高效执行各类任务。此外，我们采用进化对抗模拟器辨识来优化模拟器，使其与现实紧密对齐。训练完成后，机器人展现出多样的自然行为，成功完成四足敏捷性挑战，平均速度为1.1 m/s，并在跨栏过程中达到3.2 m/s的峰值速度。本工作代表着四足机器人迈向类动物敏捷性的重要一步，为其部署到日益复杂的真实环境开辟了道路。
英文摘要：
Achieving animal-like agility is a longstanding goal in quadrupedal robotics. While recent studies have successfully demonstrated imitation of specific behaviors, enabling robots to replicate a broader range of natural behaviors in real-world environments remains an open challenge. Here we propose an integrated controller comprising a Basic Behavior Controller (BBC) and a Task-Specific Controller (TSC) which can effectively learn diverse natural quadrupedal behaviors in an enhanced simulator and efficiently transfer them to the real world. Specifically, the BBC is trained using a novel semi-supervised generative adversarial imitation learning algorithm to extract diverse behavioral styles from raw motion capture data of real dogs, enabling smooth behavior transitions by adjusting discrete and continuous latent variable inputs. The TSC, trained via privileged learning with depth images as input, coordinates the BBC to efficiently perform various tasks. Additionally, we employ evolutionary adversarial simulator identification to optimize the simulator, aligning it closely with reality. After training, the robot exhibits diverse natural behaviors, successfully completing the quadrupedal agility challenge at an average speed of 1.1 m/s and achieving a peak speed of 3.2 m/s during hurdling. This work represents a substantial step toward animal-like agility in quadrupedal robots, opening avenues for their deployment in increasingly complex real-world environments.
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