为模仿学习创建高保真合成演示的原理性方法
A Principled Approach for Creating High-fidelity Synthetic Demonstrations for Imitation Learning
AI总结：
本文提出基于动态运动基元的框架，通过保留专家轨迹结构生成多样化演示，实现高保真渲染与几何推理的统一。
摘要：
近期3D高斯点划法（3DGS）的进步使从单个专家轨迹和短时多视角扫描生成视觉逼真演示成为可能。然而，现有基于3DGS的合成流程通常使用采样规划或轨迹优化生成新运动，这往往与专家演示路径有显著偏差。虽然这种偏差可能对不敏感于运动形状的任务可接受，但会丢弃对接触丰富和形状敏感操作至关重要的细微空间和时间结构，导致增加的演示多样性损害下游策略学习。我们主张演示合成应将专家轨迹视为强先验。基于此原理，我们提出一个框架，在生成多样化任务演示的同时显式保留专家运动结构。我们通过动态运动基元（DMPs）建模专家轨迹，并将其重新定位到重建的3DGS场景中的新目标、物体配置和视角，从而通过构造产生相位一致、形状保持的运动。为安全实现这种专家保留的多样性，在杂乱场景中，我们引入了分析障碍物感知的DMP公式，该公式直接在由3DGS表示诱导的连续密度场上操作。这使碰撞避免的同时最小扰动名义专家运动，实现逼真渲染与几何推理的统一，无需额外场景表示。我们在Spot移动机械臂上对三个逐渐增加对轨迹保真度敏感的操作任务进行了评估。与规划和优化基于的合成方法相比，我们的方法生成的轨迹偏差和碰撞率更低，并在训练扩散式视觉-运动策略时产生更高的任务成功率。
英文摘要：
Recent advances in 3D Gaussian Splatting (3DGS) have enabled visually realistic demonstration generation from a single expert trajectory and a short multi-view scan. However, existing 3DGS-based synthesis pipelines typically generate new motions using sampling-based planners or trajectory optimization, which often deviate substantially from the expert's demonstrated path. While such deviations may be acceptable for tasks insensitive to motion shape, they discard subtle spatial and temporal structure that is critical for contact-rich and shape-sensitive manipulation, causing increased demonstration diversity to harm downstream policy learning. We argue that demonstration synthesis should treat the expert trajectory as a strong prior. Building on this principle, we propose a framework that synthesizes diverse task demonstrations while explicitly preserving expert motion structure. We model the expert trajectory using Dynamic Movement Primitives (DMPs) and retarget it to new goals, object configurations, and viewpoints within a reconstructed 3DGS scene, yielding phase-consistent, shape-preserving motion by construction. To safely realize this expert-preserving diversity in cluttered scenes, we introduce an analytic obstacle-aware DMP formulation that operates directly on the continuous density field induced by the 3DGS representation. This enables collision avoidance while minimally perturbing the nominal expert motion, unifying photorealistic rendering and geometric reasoning without additional scene representations. We evaluate our approach on a Spot mobile manipulator across three manipulation tasks with increasing sensitivity to trajectory fidelity. Compared to planner- and optimization-based synthesis, our method produces trajectories with lower deviation and collision rates and yields higher task success when training diffusion-based visuomotor policies.
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