用自监督学习让机器人在陡峭地形上更灵活移动
VertiCoder: Self-Supervised Kinodynamic Representation Learning on Vertically Challenging Terrain
- 通过随机掩码和补全片段，用Transformer学环境局部特征
- 四个任务表现优于专用模型，参数少77%
- 适合需要强泛化能力的机器人运动建模场景
我们提出VertiCoder，一种用于垂直挑战地形下机器人机动性的自监督表征学习方法。通过相同的预训练流程，VertiCoder可处理四种下游任务：前向运动学建模、逆运动学建模、行为克隆以及片段重建。该方法利用TransformerEncoder对周围环境的局部上下文进行建模，通过随机掩码与下一个片段重建来实现自监督。实验表明，VertiCoder在所有四项任务中均优于专用端到端模型，且参数量减少77%。此外，在真实机器人部署中，其性能可与当前最先进的运动学建模与规划方法相媲美。这些结果验证了VertiCoder在缓解过拟合、提升多样环境与任务间泛化能力方面的有效性。
原文摘要 · Abstract (English)
We present VertiCoder, a self-supervised representation learning approach for robot mobility on vertically challenging terrain. Using the same pre-training process, VertiCoder can handle four different downstream tasks, including forward kinodynamics learning, inverse kinodynamics learning, behavior cloning, and patch reconstruction with a single representation. VertiCoder uses a TransformerEncoder to learn the local context of its surroundings by random masking and next patch reconstruction. We show that VertiCoder achieves better performance across all four different tasks compared to specialized End-to-End models with 77% fewer parameters. We also show VertiCoder's comparable performance against state-of-the-art kinodynamic modeling and planning approaches in real-world robot deployment. These results underscore the efficacy of VertiCoder in mitigating overfitting and fostering more robust generalization across diverse environmental contexts and downstream vehicle kinodynamic tasks.
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