In this paper, we introduce DynaRetarget, a complete pipeline for retargeting human motions to humanoid control policies. The core component of DynaRetarget is a novel Sampling-Based Trajectory Optimization (SBTO) framework that refines imperfect kinematic trajectories into dynamically feasible motions. SBTO incrementally advances the optimization horizon, enabling optimization over the entire trajectory for long-horizon tasks. We validate DynaRetarget by successfully retargeting hundreds of humanoid-object demonstrations and achieving higher success rates than the state of the art. The framework also generalizes across varying object properties, such as mass, size, and geometry, using the same tracking objective. This ability to robustly retarget diverse demonstrations opens the door to generating large-scale synthetic datasets of humanoid loco-manipulation trajectories, addressing a major bottleneck in real-world data collection.

翻译：本文提出DynaRetarget——一个完整的人体运动向人形机器人控制策略重定向的流水线。其核心组件是一种新型的基于采样的轨迹优化（SBTO）框架，该框架能够将不完全的运动学轨迹优化为动态可行的运动。SBTO通过逐步推进优化时域，实现了对长时域任务整个轨迹的优化。我们成功地将数百个人形物体交互演示重定向至人形机器人，并获得了相较于现有技术更高的成功率，从而验证了DynaRetarget的有效性。该框架在相同的轨迹跟踪目标下，还能泛化至不同物体属性（如质量、尺寸和几何形状）。这种鲁棒的重定向多样化演示的能力，为大规模生成人形机器人操作与移动轨迹的合成数据集开辟了道路，有效解决了真实世界数据采集的主要瓶颈。