Bayesian Experience Reuse for Learning from Multiple Demonstrators

Learning from Demonstrations (LfD) is a powerful approach for incorporating advice from experts in the form of demonstrations. However, demonstrations often come from multiple sub-optimal experts with conflicting goals, rendering them difficult to incorporate effectively in online settings. To address this, we formulate a quadratic program whose solution yields an adaptive weighting over experts, that can be used to sample experts with relevant goals. In order to compare different source and target task goals safely, we model their uncertainty using normal-inverse-gamma priors, whose posteriors are learned from demonstrations using Bayesian neural networks with a shared encoder. Our resulting approach, which we call Bayesian Experience Reuse, can be applied for LfD in static and dynamic decision-making settings. We demonstrate its effectiveness for minimizing multi-modal functions, and optimizing a high-dimensional supply chain with cost uncertainty, where it is also shown to improve upon the performance of the demonstrators' policies.

DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation

Dual-arm robot manipulation is applicable to many domains, such as industrial, medical, and home service scenes. Learning from demonstrations is a highly effective paradigm for robotic learning, where a robot learns from human actions directly and can be used autonomously for new tasks, avoiding the complicated analytical calculation for motion programming. However, the learned skills are not easy to generalize to new cases where special constraints such as varying relative distance limitation of robotic end effectors for human-like cooperative manipulations exist. In this paper, we propose a dynamic movement primitives (DMPs) based skills learning framework for redundant dual-arm robots. The method, with a coupling acceleration term to the DMPs function, is inspired by the transient performance control of Barrier Lyapunov Functions. The additional coupling acceleration term is calculated based on the constant joint distance and varying relative distance limitations of end effectors for object-approaching actions. In addition, we integrate the generated actions in joint space and the solution for a redundant dual-arm robot to complete a human-like manipulation. Simulations undertaken in Matlab and Gazebo environments certify the effectiveness of the proposed method.