AI 中文总结
DexMani框架通过迁移人类演示的接触条件可操作度演化引导强化学习，在多款机器人手上实现了更高的物体旋转成功率与更平稳的运动，且可跨本体迁移技能。
AI 中文摘要
灵巧物体旋转是一个序列接触问题：每次支撑、释放和重新接触的决策必须同时产生期望的物体运动，并为后续旋转准备好手部构型。现有的强化学习方法会在特定机器人手部本体上通过试错发现此类运动模式，但未明确考虑每次接触转换如何影响手部在后续步骤中维持物体旋转的能力。我们提出DexMani，这一框架将人类演示作为接触条件下的可操作度演化进行迁移，该先验捕捉了成功的人类接触转换如何重塑手部可用的物体旋转方向。DexMani随后学习这种可操作度演化，并将其用于引导下游强化学习，使旋转技能能够在具有不同运动学和主动接触构型的机器人本体间迁移。在Shadow Hand、Allegro Hand和XHand上，DexMani在所有评估场景中对可见和不可见物体均取得最高成功率；在LEAP Hand上，DexMani的平均成功率达57.5%，优于其他基线方法并生成更平稳的旋转运动。项目网站：this https URL
英文摘要
Dexterous object rotation is a sequential contact problem: each support, release, and re-contact decision must both produce the desired object motion, and prepare the hand configuration for continued rotation. Existing reinforcement learning methods discover such movement patterns through trial and error on specific robotic hand embodiments, without explicitly accounting for how each contact transition affects the hand's ability to sustain object rotation in subsequent steps. We introduce DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution. This prior captures how successful human contact transitions reshape the object-rotation directions available to the hand. DexMani then learns this manipulability evolution and uses it to guide downstream reinforcement learning, enabling rotation skills to be acquired across robot embodiments with distinct kinematics and active-contact configurations. Across the Shadow Hand, Allegro Hand, and XHand, DexMani achieves the highest success rates in every evaluated setting for both seen and unseen objects. DexMani reaches an average success rate of 57.5% on LEAP Hand, outperforming other baselines and producing smoother rotatory motions. Project site: https://dexmani.github.io
Comments16 pages, 17 figures
