This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constraints of the objects. To open various kinds of doors, we add encoded environment parameters that define the various environments to the input of out policy. To transfer the policy into the real world, we train an adaptation module in simulation and fine-tune the adaptation module to cut down the impact of the policy-unaware environment parameters. We design a series of experiments to validate the efficacy of our framework. Additionally, we testify to the model's performance in the real world compared to the traditional door opening method.

翻译：本文提出了一种基于学习的框架，用于在非结构化环境中让机器人自适应操作具有旋转关节的物体。我们将讨论重点放在各类柜门开启任务上。为提升深度强化学习在此场景中的性能，我们利用物体约束分析性地提供了一种高效采样方式。为开启不同类型的门，我们在策略输入中增加了编码环境参数以定义各种环境。为将策略迁移至现实世界，我们在仿真中训练了一个自适应模块，并对其微调以降低策略未知环境参数的影响。我们设计了一系列实验验证框架的有效性。此外，与传统开门方法相比，我们还在现实世界中测试了该模型的性能。