ForceWAM：从演示与游戏数据中解耦未来与响应学习以实现接触丰富型操作
ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation
AI总结：
ForceWAM从演示与游戏数据解耦未来和响应学习，通过世界模型与逆动力学模型实现接触丰富型操作，在真实机器人任务中成功率达85%-95%，添加游戏数据可提升15-45个百分点。
摘要：
专家演示展示了如何完成接触丰富型任务，但对不同动作对应的交互力响应覆盖有限；任务无关的游戏数据则提供了更广泛的接触动力学经验，且不指定任务目标。为充分利用这两类数据，我们提出ForceWAM，即力世界动作模型，它将与任务兼容的交互结果知识，与局部动作如何改变接触的知识解耦。世界模型从成功的演示中学习，联合预测未来视频与六轴力/力矩轨迹，这些未来信息共同描述了任务过程中运动与接触的预期演化。单独的逆动力学模型（IDM）从任务数据与游戏数据中学习，将当前观测及记录的视觉- wrench（力旋量）未来信息映射至对应的机器人动作，因此游戏数据为目标导向的执行提供了力响应知识。部署时，IDM会将生成的未来信息反应式地转换为动作，并在世界模型更新间利用新的力旋量反馈重复使用这些动作。在真实机器人的力调控滑动、带/不带干扰的盒子翻转及环放置任务中，ForceWAM实现了85%-95%的平均成功率，优于世界建模及反应式基线方法。我们的实验进一步表明，添加任务无关的游戏数据可将成功率提升15-45个百分点，证明了其对学习接触丰富型执行的价值。项目页面与代码可在此https URL获取。
英文摘要：
Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions. Task-agnostic play provides broader experience of contact dynamics without specifying task goals. To fully leverage these two types of data, we introduce ForceWAM, a force world action model that decouples knowledge of task-compatible interaction outcomes from knowledge of how local actions change contact. A world model learns from successful demonstrations to jointly predict future video and six-axis force/torque trajectories. Together, these futures describe the expected evolution of motion and contact during the task. A separate inverse dynamics model (IDM) learns from both task and play, mapping current observations and recorded visual-wrench futures to their associated robot actions. Play data thus provide knowledge of force responses for goal-directed execution. At deployment, the IDM reactively translates generated futures into actions, reusing them with fresh wrench feedback between world-model updates. Across real-robot force-regulated sliding, box flipping with and without disturbances, and ring placement, ForceWAM achieves $85$-$95\%$ mean success, surpassing world-modeling and reactive baselines. Our experiments further show that adding task-agnostic play data improves success by $15$-$45$ percentage points, demonstrating its value for learning contact-rich execution. Project page and code are available at https://force-wam.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
