通过自引导与自适应分块提升机器人生成式行为克隆的稳定性和响应性。
Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
- 引入自引导机制，利用历史观测增强动作准确性。
- 采用自适应分块策略，动态平衡反应速度与动作一致性。
- 在仿真与真实机器人任务中显著提升成功率与鲁棒性。
生成式行为克隆（GBC）是一种简单而有效的机器人学习框架，尤其适用于多任务场景。现有GBC方法常采用开环（OL）控制的扩散策略，即通过扩散过程生成动作并以多步为单位执行，不进行重规划。尽管该方法表现出高成功率和良好泛化能力，但其固有的随机性可能导致错误的动作采样，偶尔引发意外任务失败；此外，开环控制存在响应延迟，在噪声或动态环境中性能下降。为此，本文提出两种新技术以增强扩散策略的一致性与反应性：（1）自引导，通过利用历史观测隐式促进未来感知的行为，提升动作保真度；（2）自适应分块，当反应性的收益超过时间一致性需求时，选择性更新动作序列。大量实验表明，本方法在多种模拟与真实世界机器人操控任务中显著提升了GBC性能。代码已开源：https://github.com/junhyukso/SGAC。
原文摘要 · Abstract (English)
Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies with open-loop (OL) control, where actions are generated via a diffusion process and executed in multi-step chunks without replanning. While this approach has demonstrated strong success rates and generalization, its inherent stochasticity can result in erroneous action sampling, occasionally leading to unexpected task failures. Moreover, OL control suffers from delayed responses, which can degrade performance in noisy or dynamic environments. To address these limitations, we propose two novel techniques to enhance the consistency and reactivity of diffusion policies: (1) self-guidance, which improves action fidelity by leveraging past observations and implicitly promoting future-aware behavior; and (2) adaptive chunking, which selectively updates action sequences when the benefits of reactivity outweigh the need for temporal consistency. Extensive experiments show that our approach substantially improves GBC performance across a wide range of simulated and real-world robotic manipulation tasks. Our code is available at https://github.com/junhyukso/SGAC
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