用图扩散模型实现机器人零样本快速模仿学习
Instant Policy: In-Context Imitation Learning via Graph Diffusion
- 将示范转化为图结构，通过扩散过程生成动作策略
- 仅需1-2个示例即可完成任务学习，真实场景表现良好
- 适合需要快速适应新任务的机器人系统开发者
在大型Transformer模型展现出强大上下文学习能力的背景下，上下文模仿学习（ICIL）为机器人领域带来新机遇。本文提出Instant Policy，可在不进行额外训练的情况下，仅凭1到2个示范就即时学习新任务，其核心在于两个关键设计：一是引入图表示作为归纳偏置，将ICIL建模为可学习扩散过程的图生成问题，实现对示范、观测与动作的结构化推理；二是利用仿真中生成的伪示范（任意轨迹）构建近乎无限的训练数据池。模拟与真实实验表明，Instant Policy可快速掌握多种日常机器人任务，并可作为跨硬件平台及零样本迁移至语言定义任务的基础。代码与视频见https://www.robot-learning.uk/instant-policy。
原文摘要 · Abstract (English)
Following the impressive capabilities of in-context learning with large transformers, In-Context Imitation Learning (ICIL) is a promising opportunity for robotics. We introduce Instant Policy, which learns new tasks instantly (without further training) from just one or two demonstrations, achieving ICIL through two key components. First, we introduce inductive biases through a graph representation and model ICIL as a graph generation problem with a learned diffusion process, enabling structured reasoning over demonstrations, observations, and actions. Second, we show that such a model can be trained using pseudo-demonstrations - arbitrary trajectories generated in simulation - as a virtually infinite pool of training data. Simulated and real experiments show that Instant Policy enables rapid learning of various everyday robot tasks. We also show how it can serve as a foundation for cross-embodiment and zero-shot transfer to language-defined tasks. Code and videos are available at https://www.robot-learning.uk/instant-policy.
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。