# 保值动作量化

> 原文链接：https://robotics.hellobike.com/papers/2026-10-12-2610-17252
> 频道：papers | 更新日期：2026-10-12 | 标签：arxiv, cs.RO
> 核心摘要：提出以保持状态可达性量化动作的VAQ，并结合MCTS用于行为克隆，在物体推动基准和硬件实验中验证。

# 保值动作量化

::: v-pre
> 原文链接：[Value-Preserving Action Quantization](https://arxiv.org/abs/2610.17252)（arXiv 2610.17252 · 发布日期：2026-10-12）

**Value-Preserving Action Quantization**

提出以保持状态可达性量化动作的VAQ，并结合MCTS用于行为克隆，在物体推动基准和硬件实验中验证。

## 核心要点

- 搜索式策略学习需离散化机器人连续动作，但动作重构误差未必保留未来状态和值影响。
- 论文证明动作量化导致的价值函数误差，可由量化动作下状态可达性的重构误差紧致界定。
- 提出VAQ按状态可达性量化动作，并与MCTS结合为VAQ-MCTS，用于行为克隆。
- 物体推动基准上，VAQ-MCTS达到或超过先进方法，提升数据效率、鲁棒性并支持零样本适应。

**验证范围与局限**：验证范围为物体推动任务基准和硬件物体推动实验，真机实验为30次试验、10分钟演示数据。

## 英文摘要 (Abstract)

Search-based policy learning, such as Monte Carlo tree search (MCTS), offers the flexibility to trade runtime computation for data efficiency and adaptation to novel environments. For robot learning, however, applying search requires discretizing the continuous action space and associated dynamics, typically through action quantization. While reconstruction-based quantization is sufficient for representing the continuous action space alone, policy learning additionally requires the quantized actions to preserve their effects on future states and values. We show that such requirement is not necessarily captured by action reconstruction error. Instead, we prove that the error in the value function resulting from action quantization is tightly bounded by the reconstruction error of state reachability under the quantized actions. Based on these results, we propose value-preserving action quantization (VAQ), which quantizes actions by preserving state reachability. We further combine VAQ with MCTS (VAQ-MCTS) as a value-preserving, search-based policy learning framework for behavioral cloning. Using a wide range of object pushing tasks as a controlled benchmark, we evaluate VAQ-MCTS against state-of-the-art behavioral cloning methods. VAQ-MCTS matches or exceeds their performance under standard settings, while substantially improving data efficiency and robustness to heterogeneous demonstrations, while enabling zero-shot adaptation to novel environments. Hardware experiments further demonstrate the effectiveness of VAQ-MCTS, completing all 30 real-world object pushing trials with only 10 minutes of demonstration data.

作者：Max Muchen Sun, Jueun Kwon, Benjamin Rivière, Todd D. Murphey
:::

## 原始参考来源 (Sources)

- [arXiv 2610.17252：Value-Preserving Action Quantization](https://arxiv.org/abs/2610.17252)