外观
论文arXiv 2610.2042914 天前
离散动作何时能成为优秀的连续控制器？
原文链接：When Do Discrete Actions Make Good Continuous Controllers?（arXiv 2610.20429 · 发布日期：2026-10-14）
When Do Discrete Actions Make Good Continuous Controllers?
通过比较连续与离散策略在单任务、多任务VLA、仿真和真机中的表现，发现离散动作预测可匹敌或优于连续策略。
核心要点
- 动机是质疑机器人基础模型必须使用连续动作参数化这一常见共识。
- 方法比较单步回归与多步流的连续和离散策略，涵盖从零单任务及多任务VLA训练。
- 实验显示在各类设置中，朴素均匀分箱的离散动作预测也能匹敌甚至超过连续策略成功率。
- 研究指出难点在分解误差；离散流或classifier-free guidance可改善动作块内响应变化。
验证范围与局限：验证范围包括仿真和真实硬件上的从零单任务与多任务VLA训练。
英文摘要 (Abstract)
Though modern AI models like LLMs predict discrete tokens, today's robotic foundation models represent actions as continuous vectors. In this paper, we challenge this common consensus by asking if continuous action parametrizations are superior to their discrete counterparts. We conduct an extensive comparison of single-step regression and multi-step flow variants of both continuous and discrete policies in both from-scratch single-task and multi-task VLA training, in both simulation and on real hardware. In all regimes, we find that discrete action prediction, even the most naive, uniform-bin quantization, can match or even outperform continuous-action policies in task success rate. Moreover, we find that the challenge to discrete action policies is \emph{not} action precision, but rather factorization error introduced by product-distribution predictions across time and action dimensions. We show that, when paired with the correct interventions---iterative computation through discrete flows, or distribution sharpening through classifier-free guidance---discrete flow policies can turn this weakness into a strength, enabling more responsive action changes during a predicted action chunk. Lastly, we show that attempts to explicitly inject geometric information into discrete policies, e.g. through corruption processes that emulate continuous flow matching, do \emph{not} yield consistent improvements. Taken together, our findings question existing assumptions that robotic action prediction requires distinct design decisions compared to other AI domains, and motivates the possibility of discrete action prediction as a compelling alternative to today's continuous action parametrizations. Link for the webpage \href{https://iscoyizj.github.io/discrete-action-website/}{https://iscoyizj.github.io/discrete-action-website/}
作者：Zeji Yi, Soham Dasgupta, Xinyang Zhang, Koushil Sreenath, Guannan Qu, Xi Chen, Max Simchowitz, Guanya Shi
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