用分块生成+动态修正，让机器人更快更准地完成复杂操作。
Masked Generative Policy for Robotic Control
- 将动作视为离散符号，用掩码Transformer并行生成并只优化不确定部分。
- 在150个任务上平均成功率提升9%，推理速度加快35倍。
- 适合处理动态环境和非马尔可夫任务，对视觉缺失场景也鲁棒。
我们提出一种新的视觉-运动模仿学习框架——掩码生成策略（MGP）。将动作表示为离散标记，训练一个条件掩码Transformer，可并行生成标记，并仅快速精修低置信度部分。我们设计两种新采样范式：MGP-Short在马尔可夫任务中采用基于得分的并行掩码生成与精修；MGP-Long则单次前向传播预测完整轨迹，并根据新观测动态修正低置信度动作标记。得益于全局一致预测与强适应执行能力，MGP-Long在复杂非马尔可夫任务上表现可靠。在涵盖Meta-World与LIBERO基准的150个机器人操作任务上的广泛评估表明，相比最先进扩散模型与自回归策略，MGP实现更快推理与更高成功率：平均成功率提升9%，每序列推理时间减少最高达35倍；在动态与观测缺失环境中，平均成功率提升60%；成功解决两个其他先进方法失败的非马尔可夫场景。
原文摘要 · Abstract (English)
We present Masked Generative Policy (MGP), a novel framework for visuomotor imitation learning. We represent actions as discrete tokens, and train a conditional masked transformer that generates tokens in parallel and then rapidly refines only low-confidence tokens. We further propose two new sampling paradigms: MGP-Short, which performs parallel masked generation with score-based refinement for Markovian tasks, and MGP-Long, which predicts full trajectories in a single pass and dynamically refines low-confidence action tokens based on new observations. With globally coherent prediction and robust adaptive execution capabilities, MGP-Long enables reliable control on complex and non-Markovian tasks that prior methods struggle with. Extensive evaluations on 150 robotic manipulation tasks spanning the Meta-World and LIBERO benchmarks show that MGP achieves both rapid inference and superior success rates compared to state-of-the-art diffusion and autoregressive policies. Specifically, MGP increases the average success rate by 9% across 150 tasks while cutting per-sequence inference time by up to 35x. It further improves the average success rate by 60% in dynamic and missing-observation environments, and solves two non-Markovian scenarios where other state-of-the-art methods fail.
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。