Human-in-the-Loop Failure Recovery with Adaptive Task Allocation

TL;DR

This paper introduces ARFA, an adaptive method for assigning robot failures to human operators based on capabilities and workload, enhancing collaboration efficiency and reducing robot idle time.

Contribution

The paper presents a novel adaptive failure allocation approach that models and updates human operator capabilities for improved human-robot collaboration.

Findings

ARFA reduces robot idle time significantly.

It improves overall system performance.

It distributes workload more evenly among operators.

Abstract

Since the recent Covid-19 pandemic, mobile manipulators and humanoid assistive robots with higher levels of autonomy have increasingly been adopted for patient care and living assistance. Despite advancements in autonomy, these robots often struggle to perform reliably in dynamic and unstructured environments and require human intervention to recover from failures. Effective human-robot collaboration is essential to enable robots to receive assistance from the most competent operator, in order to reduce their workload and minimize disruptions in task execution. In this paper, we propose an adaptive method for allocating robotic failures to human operators (ARFA). Our proposed approach models the capabilities of human operators, and continuously updates these beliefs based on their actual performance for failure recovery. For every failure to be resolved, a reward function calculates expected outcomes based on operator capabilities and historical data, task urgency, and current workload distribution. The failure is then assigned to the operator with the highest expected reward. Our simulations and user studies show that ARFA outperforms random allocation, significantly reducing robot idle time, improving overall system performance, and leading to a more distributed workload among operators.