用视觉和强化学习规划步态，让机器人更稳地走不平路。
Learning a Vision-Based Footstep Planner for Hierarchical Walking Control
- 用视觉生成局部地形图，通过强化学习决定下一步落脚点。
- 在模拟和真实机器人上验证，能在复杂地形稳定行走。
- 适合做足式机器人导航、自主运动控制的研究者。
双足机器人在动态地面接触中展现穿越复杂地形的潜力。然而，现有框架通常仅依赖本体感知或使用人工设计的视觉流程，在真实环境中易失效，且难以在非结构化环境实现实时步态规划。为此，我们提出一种基于视觉的分层控制框架，结合强化学习的高层步态规划器（根据局部高程图生成落脚指令）与低层操作空间控制器（跟踪生成轨迹）。采用角动量线性倒立摆模型构建低维状态表示，以高效捕捉动态特征并降低系统复杂度。我们在欠驱动双足机器人Cassie上，通过仿真与硬件实验，在多种地形条件下评估了该方法的性能，揭示了其能力与挑战。
原文摘要 · Abstract (English)
Bipedal robots demonstrate potential in navigating challenging terrains through dynamic ground contact. However, current frameworks often depend solely on proprioception or use manually designed visual pipelines, which are fragile in real-world settings and complicate real-time footstep planning in unstructured environments. To address this problem, we present a vision-based hierarchical control framework that integrates a reinforcement learning high-level footstep planner, which generates footstep commands based on a local elevation map, with a low-level Operational Space Controller that tracks the generated trajectories. We utilize the Angular Momentum Linear Inverted Pendulum model to construct a low-dimensional state representation to capture an informative encoding of the dynamics while reducing complexity. We evaluate our method across different terrain conditions using the underactuated bipedal robot Cassie and investigate the capabilities and challenges of our approach through simulation and hardware experiments.
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