The high cost of collecting real-robot data has made robotic simulation a scalable platform for both evaluation and data generation. Yet most existing benchmarks concentrate on simple manipulation tasks such as pick-and-place, failing to capture the non-Markovian characteristics of real-world tasks and the complexity of articulated object interactions. To address this limitation, we present RuleSafe, a new articulated manipulation benchmark built upon a scalable LLM-aided simulation framework. RuleSafe features safes with diverse unlocking mechanisms, such as key locks, password locks, and logic locks, which require different multi-stage reasoning and manipulation strategies. These LLM-generated rules produce non-Markovian and long-horizon tasks that require temporal modeling and memory-based reasoning. We further propose VQ-Memory, a compact and structured temporal representation that uses vector-quantized variational autoencoders (VQ-VAEs) to encode past proprioceptive states into discrete latent tokens. This representation filters low-level noise while preserving high-level task-phase context, providing lightweight yet robust temporal cues that are compatible with existing Vision-Language-Action models (VLA). Extensive experiments on state-of-the-art VLA models and diffusion policies show that VQ-Memory consistently improves long-horizon planning, enhances generalization to unseen configurations, and enables more efficient manipulation with reduced computational cost. Project page: vqmemory.github.io

翻译：收集真实机器人数据的高昂成本使得机器人仿真成为评估和数据生成的可扩展平台。然而，现有的大多数基准测试集中于简单的操作任务，如抓取放置，未能捕捉现实世界任务的非马尔可夫特性以及关节物体交互的复杂性。为应对这一局限，我们提出了 RuleSafe，这是一个基于可扩展的 LLM 辅助仿真框架构建的新型关节操作基准测试。RuleSafe 具有多种解锁机制的保险箱，例如钥匙锁、密码锁和逻辑锁，这些机制需要不同的多阶段推理与操作策略。这些由 LLM 生成的规则产生了非马尔可夫和长视域的任务，需要时序建模和基于记忆的推理。我们进一步提出了 VQ-Memory，这是一种紧凑且结构化的时序表示方法，它使用向量量化变分自编码器（VQ-VAEs）将过去的本体感知状态编码为离散的潜在令牌。这种表示过滤了低层噪声，同时保留了高层任务阶段上下文，提供了轻量级且鲁棒的时序线索，并与现有的视觉-语言-动作模型（VLA）兼容。在最先进的 VLA 模型和扩散策略上进行的大量实验表明，VQ-Memory 持续改进了长视域规划，增强了对未见配置的泛化能力，并实现了计算成本更低的更高效操作。项目页面：vqmemory.github.io