Robot manipulation in a physically-constrained environment requires compliant manipulation. Compliant manipulation is a manipulation skill to adjust hand motion based on the force imposed by the environment. Recently, reinforcement learning (RL) has been applied to solve household operations involving compliant manipulation. However, previous RL methods have primarily focused on designing a policy for a specific operation that limits their applicability and requires separate training for every new operation. We propose a constraint-aware policy that is applicable to various unseen manipulations by grouping several manipulations together based on the type of physical constraint involved. The type of physical constraint determines the characteristic of the imposed force direction; thus, a generalized policy is trained in the environment and reward designed on the basis of this characteristic. This paper focuses on two types of physical constraints: prismatic and revolute joints. Experiments demonstrated that the same policy could successfully execute various compliant-manipulation operations, both in the simulation and reality. We believe this study is the first step toward realizing a generalized household-robot.

翻译：在物理受限环境中的机器人操作需要顺从操作。顺从操作是一种根据环境施加的力来调整手部运动的操作技能。近年来，强化学习已被应用于解决涉及顺从操作的家居任务。然而，以往强化学习方法主要专注于为特定操作设计策略，这限制了其适用性，且每个新操作都需要单独训练。我们提出了一种约束感知策略，该策略通过将多个操作按照所涉及的物理约束类型进行分组，从而适用于各种未见过的操作。物理约束的类型决定了施加力方向的特征；因此，基于这一特征训练了一个通用策略并设计了相应的奖励。本文聚焦于两种物理约束类型：棱柱关节和旋转关节。实验表明，相同的策略能够在仿真和现实中成功执行多种顺从操作任务。我们相信这项研究是实现通用家务机器人的第一步。