外观
论文arXiv 2609.283141 个月前
TANDEM框架：按需人机协同采集数据微调VLA模型成功率达60%
本文由 AI 根据公开内容整理，可能有误，请以原文为准：TANDEM: Task and Motion Planning with As-Needed Demonstrations for Efficient Vision-Language-Action Model Fine-tuning（arXiv 2609.28314）
核心内容
针对机器人基础模型训练中人类遥操作效率低及固定规划域局限问题，研究提出TANDEM系统。该方法将人类辅助建模为按需规划能力，通过预训练视觉语言模型动态补全缺失谓词与魔术算子，实现自主规划与人工执行的无缝交织。在五项超出传统TAMP能力的长程操作任务评测中，该系统显著提升了数据采集效率与模型微调效果。
技术上，TANDEM在人工介入后重新感知场景并验证预期效果，再利用示例预训练轨迹对齐规划器生成动作与目标VLA模型分布。实验表明，同等人工干预时长下其采集演示数量是全任务遥操作的2.9倍；使用每任务仅20条TANDEM演示微调预训练π_{0.5}-DROID模型，使五项长程任务的平均成功率从0%跃升至60%，验证了混合数据策略对VLA泛化的有效性。
关键看点
- 按需规划机制：利用VLM将人类辅助转化为动态魔术算子，扩展规划域以覆盖长程任务盲区，无需预设人工干预点
- 数据采集增效：在相同人类干预时间约束下，TANDEM采集的有效演示数据量达到全任务遥操作模式的2.9倍
- 小样本微调突破：仅用每任务20条混合演示微调π_{0.5}-DROID模型，五项长程操作平均成功率由0%提升至60%
- 闭环状态校验：每个人工执行阶段结束后自动重感知场景并验证谓词状态，确保自主规划恢复时的环境一致性
工程与产业观察
该工作为具身智能VLA模型提供了高效的数据工程范式。通过将符号规划与人类示范解耦再融合，解决了长尾场景数据稀缺难题。对于算法工程师，其核心价值在于证明了高质量混合数据比纯人工数据更具训练性价比，且少量精准演示即可激活预训练模型的长程推理能力，降低了垂类场景落地门槛。
英文摘要 (Abstract)
Human teleoperators spend substantial time demonstrating behaviors that robots can already perform autonomously, limiting the scalability of data collection for robot foundation models. Task and motion planning (TAMP) can automate many of these behaviors, but a fixed planning domain may not support every stage of a long-horizon manipulation task. We present TANDEM (Tamp with As-Needed Demonstrations for Efficient Model fine-tuning), a system that combines TAMP with selective human teleoperation to collect demonstrations for tasks beyond the planner's capabilities. Our key idea is to represent human assistance as an on-demand planning capability. Given a language instruction and visual observation, TANDEM uses pretrained vision-language models to extend the planning domain with missing predicates and human-executed magic operators. This allows the planner to interleave autonomous and human-executed stages without task-specific intervention points. After each human stage, TANDEM re-perceives the scene and checks whether the intended effects hold before resuming autonomous planning. To support fine-tuning vision-language-action (VLA) models, TANDEM also uses example pretraining trajectories to align planner-generated motions with the target model's pretraining distribution. We evaluate TANDEM on five long-horizon manipulation tasks beyond the TAMP domain's capabilities. On a representative long-horizon task, TANDEM collects 2.9x as many demonstrations as full-task teleoperation at the same human intervention time. Fine-tuning a pretrained π_{0.5}-DROID model on 20 TANDEM demonstrations per task increases average task success from 0% to 60% across the five tasks.
作者：Samrat Sahoo, Liang Ji, Tom Silver, Yixuan Huang
来源
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