让机器人靠前视记忆走后路，复杂地形不撞墙
Look Forward to Walk Backward: Efficient Terrain Memory for Backward Locomotion with Forward Vision
- 用前视深度与本体感知构建紧凑地形记忆
- 后向行走时无须后视摄像头仍可避障
- 适合低成本机器人实时部署
具有自身体前向深度摄像头的足式机器人可通过外感受与本体感受的耦合，在复杂地形上实现稳健的前向敏捷运动。当机器人后退时，仅前向视野无法提供前瞻信息。纯本体感知控制器在中等地面仍能保持稳定，但在复杂地形上无法充分发挥性能，且会与障碍物碰撞。我们提出 Look Forward to Walk Backward (LF2WB)：一种高效地形记忆运动框架，利用前向自身体深度与本体感知，在前进过程中写入紧凑的关联记忆，并在后退时检索该记忆实现无碰撞运动，无需后向视觉。记忆主干采用增量规则选择性更新，沿活跃子空间软删除并重写记忆状态。训练采用硬件高效的并行计算，部署时每步推理为递归常数时间，状态大小恒定，适用于低成本机器人的机载处理器。仿真与真实场景实验均验证了该方法在有限感知条件下提升后向敏捷性的有效性。
原文摘要 · Abstract (English)
Legged robots with egocentric forward-facing depth cameras can couple exteroception and proprioception to achieve robust forward agility on complex terrain. When these robots walk backward, the forward-only field of view provides no preview. Purely proprioceptive controllers can remain stable on moderate ground when moving backward but cannot fully exploit the robot's capabilities on complex terrain and must collide with obstacles. We present Look Forward to Walk Backward (LF2WB), an efficient terrain-memory locomotion framework that uses forward egocentric depth and proprioception to write a compact associative memory during forward motion and to retrieve it for collision-free backward locomotion without rearward vision. The memory backbone employs a delta-rule selective update that softly removes then writes the memory state along the active subspace. Training uses hardware-efficient parallel computation, and deployment runs recurrent, constant-time per-step inference with a constant-size state, making the approach suitable for onboard processors on low-cost robots. Experiments in both simulations and real-world scenarios demonstrate the effectiveness of our method, improving backward agility across complex terrains under limited sensing.
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