Embodying Human-Like Modes of Balance Control Through Human-In-the-Loop Dyadic Learning

Abstract

In this paper, we explore how humans and AIs trained to perform a virtual inverted pendulum (VIP) balancing task converge and differ in their learning and performance strategies. We create a visual analogue of disoriented IP balancing, as may be experienced by pilots suffering from spatial disorientation, and train AI models on data from human subjects performing a real-world disoriented balancing task. We then place the trained AI models in a dyadic human-in-the-loop (HITL) training setting. Episodes in which human subjects disagreed with AI actions were logged and used to fine-tune the AI model. Human subjects then performed the task while being given guidance from pretrained and dyadically fine-tuned versions of an AI model. We examine the effects of HITL training on AI performance, AI guidance on human performance, and the behavior patterns of human subjects and AI models during task performance. We find that in many cases, HITL training improves AI performance, AI guidance improves human performance, and after dyadic training the two converge on similar behavior patterns.