  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.

Robotics;Machine Learning