  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.
