AdaDexTrack：面向自适应与可泛化灵巧操作跟踪的动态调制
AdaDexTrack: Dynamic Modulation for Adaptive and Generalizable Dexterous Manipulation Tracking
把语言条件下的灵巧操作重构为「调制式跟踪」：用蒸馏得到的通才跟踪器承载技能，再用 RL 训练的调制器在线修正参考轨迹、物体潜表示与位置目标，可直接借鉴到视觉驱动的机器人操作与手-物交互策略中，尤其是缓解噪声参考与重定向偏差导致的漂移。
原文依据 replacing the open-loop, fixed-reference tracking with in-loop modulation that adjusts the reference, object latent, and positional target
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论语言是向机器人下达指令的一种自然方式，但将单条指令转化为长时程、接触丰富的手-物交互仍然具有挑战性：合成得到的参考轨迹带有噪声，人到机器人的重定向会引入具身偏差，而固定参考的跟踪会让小误差不断累积放大。为此，我们提出 AdaDexTrack，一个面向语言条件操作跟踪的调制器在环框架。一个经蒸馏得到的通才跟踪器充当技能载体，而一个与之紧密对齐的调制器执行三类反馈修正：参考调制（持续调整跟踪目标）、物体潜表示调制（在线自适应物体表示以调用合适的技能）以及位置目标调制（依赖状态的小幅执行精修）。跟踪器通过在大规模语言条件手-物轨迹语料上进行专家到通才的蒸馏来学习；调制器在同一任务目标下用强化学习训练，从而保证二者紧密耦合。在大规模评测中，AdaDexTrack 在未见轨迹集与未见物体集上于平均跟踪误差和成功率两方面均持续优于此前的 SOTA，展现出鲁棒性与泛化能力。我们进一步在真实硬件上展示了零样本仿真到现实迁移，其中加入调制器相比仅使用跟踪器的变体带来显著提升。AdaDexTrack 将语言条件灵巧操作重新表述为调制式跟踪，用在环调制取代开环、固定参考的跟踪，对参考、物体潜表示与位置目标进行调整，从而在含噪文本参考下实现抗漂移的执行。
英文原文摘要 1683 字符
Language is a natural way to command robots, but converting a single instruction into a long-horizon, contact-rich hand-object interaction remains challenging: synthesized references are noisy, human-to-robot retargeting introduces embodiment bias, and fixed-reference tracking lets small errors snowball. We address this with AdaDexTrack, a modulator-in-the-loop framework for language-conditioned manipulation tracking. A distilled generalist tracker serves as the skill carrier, while a tightly aligned modulator performs three feedback corrections: reference modulation (continual adjustment of what to track), object-latent modulation (online adaptation of the object representation to recruit suitable skills), and positional-target modulation (small state-dependent refinements for execution). The tracker is learned via large-scale specialist to generalist distillation on a corpus of language-conditioned hand-object trajectories; the modulator is trained with RL under the same task objective, ensuring tight coupling. Across large-scale evaluations, AdaDexTrack consistently outperforms prior SOTA on unseen-trajectory and unseen-object sets in both average tracking error and success rate, demonstrating robustness and generalization. We further show zero-shot sim-to-real transfer on real hardware, where adding the modulator yields substantial gains over a tracker-only variant. AdaDexTrack reframes language-conditioned dexterous manipulation as modulated tracking, replacing the open-loop, fixed-reference tracking with in-loop modulation that adjusts the reference, object latent, and positional target, yielding drift-resistant execution from noisy text references.