用于视觉-语言-动作学习的从简单到复杂的结构化演示
Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning
AI总结：
研究视觉-语言-动作模型中演示的收集与组织问题，提出用双臂机器人平台的从简单到复杂结构化演示收集策略，依三原则组织数据，提升了学习效率与稳定性。
摘要：
视觉-语言-动作（VLA）模型在机器人操作中展现强大能力。现有研究多关注模型架构等，忽视演示收集与组织。本文提出VLA学习的从简单到复杂结构化演示收集策略，通过三原则组织数据，实验表明相比直接收集端到端轨迹的基线方法有改进。
英文摘要：
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation. Existing research has primarily focused on improving model architectures, training strategies, and dataset scale, while little attention has been paid to how demonstrations are collected and organized. We identify demonstration organization as a fundamental yet overlooked aspect of imitation learning, as it directly affects policy learning efficiency, training stability, and policy generalization. To address this gap, we propose a simple-to-complex structured demonstration collection strategy for VLA learning using a dual-arm robotic platform. Our approach systematically organizes data through three general principles: (i) decomposing complex manipulation tasks into progressively learnable sub-skills, (ii) standardizing the interaction environment to reduce unnecessary variability, and (iii) organizing demonstrations according to progressively increasing task complexity. This structured design enables VLA models to first acquire fundamental manipulation skills before learning increasingly complex task compositions, facilitating more effective learning of long-horizon manipulation tasks. We evaluate the proposed strategy on two representative robotic manipulation tasks: block grasping and sorting, and towel folding. Experimental results show consistent improvements in task success rate and training stability compared with the baseline method of directly collecting end-to-end complete task trajectories. These findings highlight demonstration organization as a previously underexplored but important factor in VLA learning and provide practical insights into efficient skill acquisition, scalable dataset construction, and long-horizon robotic manipulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
