通过接触构型调控实现物体操作：多接触与间歇接触
Object manipulation through contact configuration regulation: multiple and intermittent contacts
AI总结：
本研究改进基于接触构型调控的未知物体操作方法，通过结合因子图估计框架、有限视觉反馈与力/力矩及本体感知，支持间歇、多接触等复杂交互与更多接触几何形态，拓展了机器人操作任务范围，并通过消融实验明确各反馈的贡献。
摘要：
在本研究中，我们基于此前提出的通过接触构型调控操作未知物体的方法展开研究——接触构型调控即对机器人、物体与环境之间所有接触的位置、几何形态和模式进行估计与控制。我们进一步开发了估计器与控制器，以支持通过更复杂的接触交互完成操作，包括机器人与物体之间的间歇接触，以及物体与环境之间的多接触场景。此外，我们在每个接触界面支持更多种类的接触几何形态。这一目标通过基于因子图的估计框架实现，该框架对接触的互补运动学约束与 wrench（力旋量）约束进行推理，以预测当前接触构型。我们还引入了有限的视觉反馈作为辅助；将其与可用的力/力矩（F/T）传感及机器人本体感知相结合后，能够区分此前无法辨别的接触模式。我们在操作平台上实现了这一改进后的框架，并证明其可让机器人执行更广泛的操作任务，包括利用墙面作为支撑重新调整物体朝向，或是调控物体与地面之间的接触几何形态。最后，我们开展了消融实验，以明确视觉反馈与触觉反馈在该操作框架中的各自贡献。我们的代码可在以下链接获取：https://github.com/mcubelab/pbal。
英文摘要：
In this work, we build on our method for manipulating unknown objects via contact configuration regulation: the estimation and control of the location, geometry, and mode of all contacts between the robot, object, and environment. We further develop our estimator and controller to enable manipulation through more complex contact interactions, including intermittent contact between the robot/object, and multiple contacts between the object/environment. In addition, we support a larger set of contact geometries at each interface. This is accomplished through a factor graph based estimation framework that reasons about the complementary kinematic and wrench constraints of contact to predict the current contact configuration. We are aided by the incorporation of a limited amount of visual feedback; which when combined with the available F/T sensing and robot proprioception, allows us to differentiate contact modes that were previously indistinguishable. We implement this revamped framework on our manipulation platform, and demonstrate that it allows the robot to perform a wider set of manipulation tasks. This includes, using a wall as a support to re-orient an object, or regulating the contact geometry between the object and the ground. Finally, we conduct ablation studies to understand the contributions from visual and tactile feedback in our manipulation framework. Our code can be found at: https://github.com/mcubelab/pbal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
