Model-free control strategies such as reinforcement learning have shown the
ability to learn control strategies without requiring an accurate model or
simulator of the world. While this is appealing due to the lack of modeling
requirements, such methods can be sample inefficient, making them impractical
in many real-world domains. On the other hand, model-based control techniques
leveraging accurate simulators can circumvent these challenges and use a large
amount of cheap simulation data to learn controllers that can effectively
transfer to the real world. The challenge with such model-based techniques is
the requirement for an extremely accurate simulation, requiring both the
specification of appropriate simulation assets and physical parameters. This
requires considerable human effort to design for every environment being
considered. In this work, we propose a learning system that can leverage a
small amount of real-world data to autonomously refine a simulation model and
then plan an accurate control strategy that can be deployed in the real world.
Our approach critically relies on utilizing an initial (possibly inaccurate)
simulator to design effective exploration policies that, when deployed in the
real world, collect high-quality data. We demonstrate the efficacy of this
paradigm in identifying articulation, mass, and other physical parameters in
several challenging robotic manipulation tasks, and illustrate that only a
small amount of real-world data can allow for effective sim-to-real transfer.
Project website at this https URL

通过利用少量真实世界数据来自动完善模拟模型并规划准确的控制策略，在多个具有挑战性的机器人操作任务中，我们展示了这种模式对识别关节、质量和其他物理参数的有效性，并且说明了只需少量真实世界数据即可进行有效的模拟到真实世界的转换。

ASID：机器人操作中的系统辨识主动探索

ASID: Active Exploration for System Identification in Robotic  Manipulation

Model-based methods are the dominant paradigm for controlling robotic
systems, though their efficacy depends heavily on the accuracy of the model
used. Deep neural networks have been used to learn models of robot dynamics
from data, but they suffer from data-inefficiency and the difficulty to
incorporate prior knowledge. We introduce Structured Mechanical Models, a
flexible model class for mechanical systems that are data-efficient, easily
amenable to prior knowledge, and easily usable with model-based control
techniques. The goal of this work is to demonstrate the benefits of using
Structured Mechanical Models in lieu of black-box neural networks when modeling
robot dynamics. We demonstrate that they generalize better from limited data
and yield more reliable model-based controllers on a variety of simulated
robotic domains.

本文说明了使用结构化机械模型取代黑盒神经网络在机器人动力学建模上具有的优势，包括数据效率、易于应用先前知识和易于与基于模型的控制技术结合使用。在多个仿真机器人领域中，该方法可以从有限的数据中更好地推广和产生更可靠的基于模型的控制器。