In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-level navigation commands, the robot is able to safely locomote to a target location based on frames from a depth camera without any explicit mapping of the environment. First, the sequence of images and the current trajectory of the camera are fused to form a model of the world using state representation learning. The output of this lightweight module is then directly fed into a target-reaching and obstacle-avoiding policy trained with reinforcement learning. We show that decoupling the pipeline into these components results in a sample efficient policy learning stage that can be fully trained in simulation in just a dozen minutes. The key part is the state representation, which is trained to not only estimate the hidden state of the world in an unsupervised fashion, but also helps bridging the reality gap, enabling successful sim-to-real transfer. In our experiments with the quadrupedal robot ANYmal in simulation and in reality, we show that our system can handle noisy depth images, avoid dynamic obstacles unseen during training, and is endowed with local spatial awareness.

中文翻译：

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在混乱和动态环境中学习状态表示和导航

在这项工作中，我们提出了一个基于学习的管道，以在杂乱的环境中（具有静态和动态障碍物）使用四足机器人实现本地导航。有了高级导航命令，该机器人就可以基于深度相机的帧安全地定位到目标位置，而无需对环境进行任何明确的映射。首先，使用状态表示学习将图像序列和相机的当前轨迹融合在一起，以形成世界模型。然后，将这个轻量级模块的输出直接输入经过强化学习训练的目标达成和避障策略。我们显示，将管道解耦到这些组件会导致一个有效的示例策略学习阶段，可以在十几分钟内对它进行全面的模拟培训。关键部分是状态表示，经过培训，不仅可以以无人监督的方式估计世界的隐藏状态，而且还可以弥合现实差距，从而实现从模拟到真实的成功转移。在模拟和现实中使用四足机器人ANYmal进行的实验中，我们证明了我们的系统可以处理嘈杂的深度图像，避免训练过程中看不见的动态障碍，并具有局部空间感知能力。