Robots that assist humans will need to interact with articulated objects such as cabinets or microwaves. Early work on creating systems for doing so used proprioceptive sensing to estimate joint mechanisms during contact. However, nowadays, almost all systems use only vision and no longer consider proprioceptive information during contact. We believe that proprioceptive information during contact is a valuable source of information and did not find clear motivation for not using it in the literature. Therefore, in this paper, we create a system that, starting from a given grasp, uses proprioceptive sensing to open cabinets with a position-controlled robot and a parallel gripper. We perform a qualitative evaluation of this system, where we find that slip between the gripper and handle limits the performance. Nonetheless, we find that the system already performs quite well. This poses the question: should we make more use of proprioceptive information during contact in articulated object manipulation systems, or is it not worth the added complexity, and can we manage with vision alone? We do not have an answer to this question, but we hope to spark some discussion on the matter. The codebase and videos of the system are available at https://tlpss.github.io/revisiting-proprioception-for-articulated-manipulation/.

翻译：辅助人类的机器人需要与柜子或微波炉等铰接物体交互。早期构建此类系统的研究工作利用本体感知来估计接触过程中的关节机构。然而，现今几乎所有系统仅依赖视觉，在接触过程中不再考虑本体感知信息。我们认为接触过程中的本体感知是宝贵的信息来源，且在文献中未发现明确理由解释为何弃之不用。因此，本文构建了一个系统：从给定的抓取点出发，利用本体感知引导位置控制机器人及平行夹爪打开柜子。我们对该系统进行了定性评估，发现夹爪与把手之间的滑动限制了性能表现，但系统整体表现依然良好。这引出一个问题：在铰接物体操作系统中，是否应更充分地利用接触过程中的本体感知信息？抑或这种复杂度提升得不偿失，仅凭视觉即可应对？我们对此尚无答案，但期望引发相关讨论。系统代码库及演示视频见https://tlpss.github.io/revisiting-proprioception-for-articulated-manipulation/。