基于真实世界视觉数据学习的可通行性感知足式导航
Traversability-Aware Legged Navigation by Learning from Real-World Visual Data
AI总结：
针对四足机器人复杂地形导航中能量成本与机动性平衡的挑战，提出基于机器人移动控制器值函数的可通行性估计器，并将其集成到多阶段训练的RGBD导航框架中，利用真实世界多模态数据实现高效学习与泛化。
摘要：
足式移动带来的增强机动性使四足机器人能够在复杂和非结构化环境中导航。然而，在优化敏捷移动的同时考虑穿越不同地形带来的不同能量成本，仍然是一个未解决的挑战。大多数先前的工作侧重于基于人类标注的环境特征，通过可通行性成本估计来规划轨迹。然而，这种以人为中心的方法是不充分的，因为它没有考虑机器人移动控制器在具有挑战性的地形上的能力差异。为了解决这个问题，我们以机器人为中心的方式，基于机器人移动控制器的值函数开发了一种新颖的可通行性估计器。该估计器被集成到一个新的基于学习的RGBD导航框架中。该框架采用多个训练阶段来开发一个规划器，引导机器人在避开障碍物和难以穿越的地形的同时到达目标。导航规划器的训练直接在真实世界中进行，使用了一种利用在线数据和离线数据集的样本高效强化学习方法。通过广泛的基准测试，我们证明所提出的框架在准确的可通行性成本估计和从多模态数据（包括机器人的彩色和深度视觉，以及本体感受反馈）中高效学习以进行真实世界训练方面实现了最佳性能。使用所提出的方法，四足机器人学会在具有挑战性地形（难以仅使用深度视觉进行分类）的各种真实世界环境中通过试错来执行可通行性感知导航。此外，机器人展示了将学习到的导航技能泛化到未见场景的能力。视频可在 https://youtu.be/RSqnIWZ1qks 找到。
英文摘要：
The enhanced mobility brought by legged locomotion empowers quadrupedal robots to navigate through complex and unstructured environments. However, optimizing agile locomotion while accounting for the varying energy costs of traversing different terrains remains an open challenge. Most previous work focuses on planning trajectories with traversability cost estimation based on human-labeled environmental features. However, this human-centric approach is insufficient because it does not account for the varying capabilities of the robot locomotion controllers over challenging terrains. To address this, we develop a novel traversability estimator in a robot-centric manner, based on the value function of the robot's locomotion controller. This estimator is integrated into a new learning-based RGBD navigation framework. The framework employs multiple training stages to develop a planner that guides the robot in avoiding obstacles and hard-to-traverse terrains while reaching its goals. The training of the navigation planner is directly performed in the real world using a sample efficient reinforcement learning method that utilizes both online data and offline datasets. Through extensive benchmarking, we demonstrate that the proposed framework achieves the best performance in accurate traversability cost estimation and efficient learning from multi-modal data (including the robot's color and depth vision, as well as proprioceptive feedback) for real-world training. Using the proposed method, a quadrupedal robot learns to perform traversability-aware navigation through trial and error in various real-world environments with challenging terrains that are difficult to classify using depth vision alone. Moreover, the robot demonstrates the ability to generalize the learned navigation skills to unseen scenarios. Video can be found at https://youtu.be/RSqnIWZ1qks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
