For tasks where the dynamics of multiple agents are physically coupled, e.g., in cooperative manipulation, the coordination between the individual agents becomes crucial, which requires exact knowledge of the interaction dynamics. This problem is typically addressed using centralized estimators, which can negatively impact the flexibility and robustness of the overall system. To overcome this shortcoming, we propose a novel distributed learning framework for the exemplary task of cooperative manipulation using Bayesian principles. Using only local state information each agent obtains an estimate of the object dynamics and grasp kinematics. These local estimates are combined using dynamic average consensus. Due to the strong probabilistic foundation of the method, each estimate of the object dynamics and grasp kinematics is accompanied by a measure of uncertainty, which allows to guarantee a bounded prediction error with high probability. Moreover, the Bayesian principles directly allow iterative learning with constant complexity, such that the proposed learning method can be used online in real-time applications. The effectiveness of the approach is demonstrated in a simulated cooperative manipulation task.
翻译:对于多种物剂的动态是实际结合在一起的任务,例如,在合作操纵中,个别物剂之间的协调变得至关重要,这需要确切了解相互作用的动态。这个问题通常通过集中的测算器来解决,这可能会对整个系统的灵活性和稳健性产生消极影响。为了克服这一缺陷,我们提议为利用贝叶斯原则进行合作操纵的示范性任务建立一个新颖的分布式学习框架。每个物剂仅使用当地的国家信息,就能估计物体的动态和掌握运动学。这些地方性估计是使用动态平均共识加以结合的。由于这种方法具有很强的概率基础,对物体动态和掌握运动学的每一项估计都伴随着一种不确定性的测量,从而能够保证极有可能发生受约束的预测错误。此外,巴伊斯原则直接允许不断复杂地进行互动学习,这样就可以在实时应用中使用拟议的学习方法。该方法的有效性表现在模拟合作操纵任务中。