让机器人在陌生环境中稳健行走，靠的是新型变压器网络。
Generalized Locomotion in Out-of-distribution Conditions with Robust Transformer
- 用身体分块和一致丢弃策略改进Transformer结构。
- 仅在少数动态环境下训练，就能适应多种未知挑战。
- 适合需要强泛化能力的四足、六足机器人应用。
为在真实世界中成功运行，机器人必须应对训练时未见的情况。对于腿式机器人，主要挑战包括动态间隙和感知间隙。本文研究在这些新情境下的鲁棒运动问题。与以往依赖复杂训练与适应技术的方法不同，我们从网络模型角度出发，提出一种名为ROLT（Robust Locomotion Transformer）的Transformer变体。ROLT引入两个关键设计：身体分块（body tokenization）支持不同肢体间知识共享，提升网络泛化能力；新颖的丢弃策略增强策略对未见感知噪声的鲁棒性。我们在四足和六足机器人上进行了大量实验。结果表明，尽管仅在少数动态场景下训练，该策略仍能良好泛化至多种未见动态条件；且即使训练时使用清晰观测，测试时也能有效处理严重感知噪声。
原文摘要 · Abstract (English)
To succeed in the real world, robots must deal with situations that differ from those seen during training. Those out-of-distribution situations for legged robot mainly include challenging dynamic gaps and perceptual gaps. Here we study the problem of robust locomotion in such novel situations. While previous methods usually rely on designing elaborate training and adaptation techniques, we approach the problem from a network model perspective. Our approach, RObust Locomotion Transformer(ROLT),a variation of transformer,could achieve robustness in a variety of unseen conditions. ROLT introduces two key designs: body tokenization and consistent dropout. Body tokenization supports knowledge share across different limbs, which boosts generalization ability of the network. Meanwhile, a novel dropout strategy enhances the policy's robustness to unseen perceptual noise. We conduct extensive experiments both on quadruped and hexapod robots. Results demonstrate that ROLT is more robust than existing methods. Although trained in only a few dynamic settings, the learned policy generalizes well to multiple unseen dynamic conditions. Additionally, despite training with clean observations, the model handles challenging corruption noise during testing.
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