A fundamental task in robotics is to navigate between two locations. In particular, real-world navigation can require long-horizon planning using high-dimensional RGB images, which poses a substantial challenge for end-to-end learning-based approaches. Current semi-parametric methods instead achieve long-horizon navigation by combining learned modules with a topological memory of the environment, often represented as a graph over previously collected images. However, using these graphs in practice typically involves tuning a number of pruning heuristics to avoid spurious edges, limit runtime memory usage and allow reasonably fast graph queries. In this work, we present One-4-All (O4A), a method leveraging self-supervised and manifold learning to obtain a graph-free, end-to-end navigation pipeline in which the goal is specified as an image. Navigation is achieved by greedily minimizing a potential function defined continuously over the O4A latent space. Our system is trained offline on non-expert exploration sequences of RGB data and controls, and does not require any depth or pose measurements. We show that O4A can reach long-range goals in 8 simulated Gibson indoor environments, and further demonstrate successful real-world navigation using a Jackal UGV platform.

翻译：机器人领域的一项基础任务是在两个位置之间导航。特别是，真实世界中的导航可能涉及使用高维RGB图像进行长程规划，这对基于端到端学习的方法提出了重大挑战。当前半参数化方法通过将学习模块与环境拓扑记忆（通常表示为基于先前采集图像的图结构）相结合来实现长程导航。然而，在实际使用这些图时，通常需要调整一系列剪枝启发式规则以避免伪边、限制运行时内存使用并实现较快的图查询。本文提出One-4-All（O4A）方法，利用自监督学习和流形学习构建无图结构的端到端导航管线，其中目标以图像形式指定。导航通过贪心最小化在O4A潜在空间上连续定义的势能函数来实现。该系统基于非专家探索序列的RGB数据及控制指令进行离线训练，无需任何深度或位姿测量。实验表明，O4A可在8个模拟Gibson室内环境中实现远程目标导航，并进一步在Jackal UGV平台上成功演示了真实世界导航。