用物理仿真生成高质量机器人抓取数据，跨设备直接部署成功率达90%以上。
Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization
- 融合虚拟现实演示与轨迹优化，自动生成适配多种机器人的数据
- 在多臂和浮空手任务中训练的扩散策略零样本部署成功率超90%
- 适合需跨硬件迁移的机器人操控研究者快速获取高质量数据
我们提出一种低成本的数据生成流程，结合物理仿真、人类示范与模型规划，高效生成大规模高质量接触丰富型机器人操作数据集。基于少量可在虚拟现实中采集的可泛化人体示范，通过基于优化的运动学重定向与轨迹优化，将示范适配至不同机器人本体和物理参数。该过程生成多样且物理一致的数据集，支持跨本体数据迁移，并可复用以往不同硬件配置下的数据。通过在生成数据集上训练扩散策略，在多种机器人本体（包括浮动Allegro手和双臂机械臂）上完成高难度接触密集型操作任务。训练策略在真实双臂iiwa机械臂上实现零样本部署，仅需少量人工干预即达到高成功率。
原文摘要 · Abstract (English)
We present a low-cost data generation pipeline that integrates physics-based simulation, human demonstrations, and model-based planning to efficiently generate large-scale, high-quality datasets for contact-rich robotic manipulation tasks. Starting with a small number of embodiment-flexible human demonstrations collected in a virtual reality simulation environment, the pipeline refines these demonstrations using optimization-based kinematic retargeting and trajectory optimization to adapt them across various robot embodiments and physical parameters. This process yields a diverse, physically consistent dataset that enables cross-embodiment data transfer, and offers the potential to reuse legacy datasets collected under different hardware configurations or physical parameters. We validate the pipeline's effectiveness by training diffusion policies from the generated datasets for challenging contact-rich manipulation tasks across multiple robot embodiments, including a floating Allegro hand and bimanual robot arms. The trained policies are deployed zero-shot on hardware for bimanual iiwa arms, achieving high success rates with minimal human input. Project website: https://lujieyang.github.io/physicsgen/.
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