Uncertainty-Aware Haptic Shared Control with Humanoid Robots for Flexible Object Manipulation

We propose a haptic shared control system that pre- dicts human manipulation intentions using a neural network and adaptively presents haptic guidance to achieve smooth robot con- trol remotely. Although the haptic shared control has garnered increasing attention as a method to improve operability in remote operations,incorrectguidancecanworsenoperability.Inthisstudy, we dynamically switch the strength of haptic guidance presentation depending on the uncertainty of the inference results of the neu- ral network. Thus, we weaken the haptic guidance presentation strength for predictions in which the neural network lacks con- fidence and strengthen it for those with high confidence, thereby achieving guidance presentation that does not impede human ma- nipulation. As a result of experiments using the Nextage OPEN upper-body humanoid robot, in a task involving folding a flexible object, we succeeded in reducing task execution time by 17.1% compared to that with an existing method that determines the strength of haptic guidance presentation without considering the confidence of the neural network.