让机器人在多任务下安全运行，还能自动避障。
Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions
- 用神经网络构建可保证稳定性的动力系统。
- 支持多个任务切换，且在复杂环境中避免碰撞。
- 适合对安全性要求高的自动驾驶与机器人控制场景。
当完全自主的机器人需避免不良或潜在危险行为时，稳定性保障至关重要。我们此前提出了神经契约动力系统（NCDS），一种能保证契约稳定性的神经网络架构，使基于示范学习的方法可直接获得稳定性保障。然而，早期工作仍存在若干未解问题。本文在此基础上深入解释了NCDS，并引入更精细的正则化、适用于多任务的条件变体，以及基于不确定性的隐空间障碍物规避方法。实验验证，该系统兼具普通神经网络的灵活性，同时提供自主机器人所需的稳定性保障。
原文摘要 · Abstract (English)
Stability guarantees are crucial when ensuring that a fully autonomous robot does not take undesirable or potentially harmful actions. We recently proposed the Neural Contractive Dynamical Systems (NCDS), which is a neural network architecture that guarantees contractive stability. With this, learning-from-demonstrations approaches can trivially provide stability guarantees. However, our early work left several unanswered questions, which we here address. Beyond providing an in-depth explanation of NCDS, this paper extends the framework with more careful regularization, a conditional variant of the framework for handling multiple tasks, and an uncertainty-driven approach to latent obstacle avoidance. Experiments verify that the developed system has the flexibility of ordinary neural networks while providing the stability guarantees needed for autonomous robotics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。