CAREER: Apprenticeship Learning for Robotic Manipulation of Deformable Objects

该项目考虑了学徒学习的问题，其中机器人首先获得任务的演示，并且应该从这些演示中学习如何在新的但类似的情况下执行该任务。 这一领域的工作已经显示出巨大的前景，包括在直升机控制方面，它使最好的人类飞行员能够进行自主直升机特技飞行。 然而，基本的限制仍然存在，机器人操纵可变形物体的能力目前仍远低于人类水平。所遵循的方法建立在非刚性配准算法的基础上，并对其进行了扩展，该算法可以捕获具有可变形对象的场景如何相互关联。 这种配准被外推以将所演示的操纵轨迹变形为用于新场景的良好轨迹。 开发了新的机器学习算法，以便能够选择最佳训练演示和最佳变形目标，同时考虑外部约束，例如避免碰撞和满足关节限制。正在为大规模的示范数据收集建立基础设施，并为特定任务需要多少数据制定理论和经验特征。 考虑的具体挑战任务是打结、布料和织物操作、外科手术和小型外科手术。结果将被纳入PI的研究生机器人课程，源代码将与机器人社区共享。

This project considers the problem of apprenticeship learning, in which a robot first gets access to demonstrations of a task and ought to learn from these demonstrations how to perform that task in new, yet similar, situations. This line of work has already shown significant promise, including in helicopter control where it enabled autonomous helicopter aerobatics at the level of the best human pilots. However, fundamental limitations remain, and robotic capabilities to manipulate deformable objects are currently still well below human level. The approach followed builds on, and extends, non-rigid registration algorithms, which can capture how scenes with deformable objects relate to each other. Such registration is extrapolated to morph a demonstrated manipulation trajectory into a good trajectory for a new scene. New machine learning algorithms are developed to enable choosing the optimal training demonstration and the optimal morphing objective while accounting for external constraints, such as avoiding collisions and satisfying joint limits. Infrastructure is being built for large-scale data collection of demonstrations and theoretical and empirical characterizations are developed for how much data is needed for a given task. Concrete challenge tasks considered are knot tying, cloth and fabric manipulation, surgical suturing, and small surgical procedures. Results will be incorporated into the PI's graduate robotics course and the source code will be shared with the robotics community.