From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms.

中文翻译：

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共享工作空间中机器人的目标导向的推理与合作：基于内部仿真的神经框架。

从家庭中的社交用餐到生产线中的产品组装，目标导向的推理以及与其他工作人员在共享工作空间中的合作是我们日常活动中无处不在的方面。此类行为的关键在于，能够根据不断发展的环境自发地预测自己以及交互伙伴可以做什么，并利用此类信息参与面向目标的动作序列。在两个机器人共同在一个共享的工作空间中组装对象的工业任务中，我们描述了一种基于生物启发的神经体系结构，用于基于多个内部模型（主要是机器人的身体及其周围空间）之间的耦合相互作用进行目标定向的行动计划。学习（每个机器人的身体和个人空间的内部模型）共同通过感觉运动探索的过程，然后在与共同目标的背景下两个工业机器人的潜在动作的可行性和后果相关的一系列预期中使用。随后的行为在一个实际的工业场景中得到了证明，其中两个机器人正在从分散在其工作区中的多个组成对象（保险丝，保险丝座）组装工业保险丝盒。在空间上无结构且随时间演变的装配场景中，机器人采用基于奖励的动力学来计划和预测在什么情况下要对哪些对象进行操作，从而成功完成尽可能多的装配。现有的空间设置从根本上需要规划无碰撞的轨迹，并避免机器人之间的潜在碰撞。此外，一个有趣的场景，其中组装目标不能由两个机器人中的任何一个单独实现，而只有在它们有意义地协作时才可以实现，用于演示感知，多个内部模型的模拟以及由此产生的两个机器人的目标导向动作之间的相互作用。最后，将所提出的神经框架与典型工程解决方案进行基准测试，以评估其在组装任务中的性能。该框架为神经科学的新兴成果提供了计算前景，这些成果与身体图式和人际空间的学习和使用有关，用于行为和预测的具体模拟。虽然此处报告的实验专门针对该架构进行了复杂的计划任务，