We introduce a Learning from Demonstration (LfD) approach for contact-rich
manipulation tasks with articulated mechanisms. The extracted policy from a
single human demonstration generalizes to different mechanisms of the same type
and is robust against environmental variations. The key to achieving such
generalization and robustness from a single human demonstration is to
autonomously augment the initial demonstration to gather additional information
through purposefully interacting with the environment. Our real-world
experiments on complex mechanisms with multi-DOF demonstrate that our approach
can reliably accomplish the task in a changing environment. Videos are
available at the: this https URL

文章介绍了一种学习自演示 (LfD) 方法，用于应对带有关节机制的接触丰富的操作任务。从单个人类演示中提取的策略可以推广到相同类型的不同机制，并且对环境变化具有鲁棒性，其关键是通过与环境有目的地交互来自主增强初始演示以收集附加信息。针对多 DOF 的复杂机制的实际实验表明，我们的方法可以可靠地完成任务。

基于环境限制的示教学习增强

Augmentation for Learning From Demonstration with Environmental Constraints

Being able to reproduce physical phenomena ranging from light interaction to
contact mechanics, simulators are becoming increasingly useful in more and more
application domains where real-world interaction or labeled data are difficult
to obtain. Despite recent progress, significant human effort is needed to
configure simulators to accurately reproduce real-world behavior. We introduce
a pipeline that combines inverse rendering with differentiable simulation to
create digital twins of real-world articulated mechanisms from depth or RGB
videos. Our approach automatically discovers joint types and estimates their
kinematic parameters, while the dynamic properties of the overall mechanism are
tuned to attain physically accurate simulations. Control policies optimized in
our derived simulation transfer successfully back to the original system, as we
demonstrate on a simulated system. Further, our approach accurately
reconstructs the kinematic tree of an articulated mechanism being manipulated
by a robot, and highly nonlinear dynamics of a real-world coupled pendulum
mechanism.
Website: this https URL

本研究提出通过使用逆向渲染和可微分仿真相结合的方法，从深度或 RGB 视频中创建真实世界关节机构的数字孪生，该方法可以自动发现关节类型和估计其运动学参数，并调整整个机构的动态特性以实现物理上准确的仿真。