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，一个将人体运动重定向至人形机器人控制策略的完整流程。该系统的核心是基于采样的轨迹优化框架，能够将不完美的运动学轨迹优化为动态可行的动作。该框架通过逐步推进优化时域，实现对长时程任务的完整轨迹优化。我们通过成功重定向数百组人形机器人-物体交互演示验证了本方法，其成功率优于现有最优技术。该框架使用相同的跟踪目标，能够泛化至不同物体属性，如质量、尺寸和几何形状。这种对多样化演示的鲁棒重定向能力，为生成大规模合成人形机器人移动操作轨迹数据集开辟了道路，解决了现实世界数据收集的主要瓶颈。