发表机构
Northeastern University; Sony AI America Inc; Sony Interactive Entertainment Inc; Sony Corporation of America; Sony Group Corporation（东北大学; 索尼AI美国公司; 索尼互动娱乐公司; 索尼美国公司; 索尼集团公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出语言驱动的物理仿真角色控制方法LYRIC，通过流匹配控制器实现接触丰富的全身物体交互，在OMOMO数据集上达到90.3%任务成功率，优于基线，并支持零样本迁移。
AI 中文摘要
我们提出了LYRIC，一种用于语言驱动的物理仿真接触丰富交互控制的生成式流匹配控制器，它使仿真角色能够根据自由形式的语言指令和稀疏的终端物体目标，执行接触丰富的全身物体交互。为了从不完美的动作捕捉参考中获取可靠的专家轨迹，我们训练了一个单一跟踪策略，该策略使用几何条件交互奖励，并在手-物体接触附近放松参考跟踪。为了在不指定全身运动学参考的情况下引导交互进程，我们将控制器分解为一个任务级规划器和一个动作生成器，前者预测短时域的物体和人形根轨迹，后者在闭环中解析全身运动和接触。在行为克隆之后，我们冻结规划器，并利用规划器的预测作为中间任务进程的稳定监督，对动作生成器进行策略上的后调优。在受控的OMOMO评估中，我们的跟踪器达到了64.3%的成功率，而InterMimic重新实现为53.2%，同时一个统一策略在完整OMOMO数据集上达到了76.5%。在保留的测试集上，LYRIC达到了90.3%的任务成功率，而最强的匹配运动学规划器基线为74.2%，并且具有更好的语义对齐和运动质量。无需重新训练，该控制器还支持测试时的物体路点引导。定性结果进一步展示了鲁棒、自然的接触丰富交互以及对新物体形状的零样本迁移。网页可在以下网址获取：此https URL。
英文摘要
We present LYRIC, a generative flow-matching controller for language-driven physics-based contact-rich interaction control, that enables simulated characters to perform contact-rich whole-body object interactions from a free-form language instruction and a sparse terminal object goal. To obtain reliable expert trajectories from imperfect motion-capture references, a single tracking policy is trained using geometry-conditioned interaction rewards and relaxed reference tracking near hand-object contact. To guide interaction progress without prescribing a full-body kinematic reference, we factorize the controller into a task-level planner that predicts short-horizon object and humanoid-root trajectories, and an action generator that resolves whole-body motion and contacts in closed loop. After behavior cloning, we freeze the planner and post-tune the action generator on policy using the planner's predictions as stable supervision for intermediate task progression. In a controlled OMOMO evaluation, our tracker achieves 64.3% success compared with 53.2% for an InterMimic reimplementation, while a unified policy achieves 76.5% on the full OMOMO dataset. On the held-out split, LYRIC achieves 90.3% task success, compared with 74.2% for the strongest matched kinematic-planner baseline, with better semantic alignment and motion quality. Without retraining, the controller also supports test-time object-waypoint guidance. Qualitative results further demonstrate robust, natural contact-rich interactions and zero-shot transfer to novel object shapes. The webpage is available at https://neu-vi.github.io/LYRIC/
