Offline NEURO-LOOP

NEURO-LOOP is a reinforcement learning framework that integrates implicit neural feedback into robotic and artificial agent policy learning. It pulls from previous work by using a person’s brain signals and an agent’s behavior without requiring explicit guidance. Offline NEURO-LOOP uses fNIRS data from a dataset instead of a real-time loop.

Traditional interactive RL often depends on overt feedback such as ratings, demonstrations, or button presses. Instead, NEURO-LOOP treats passive neural activity as a supervisory signal. This enables agents to adapt behavior to a user’s expectations as they naturally observe the agent’s behavior.

The offline NEURO-LOOP framework uses fNIRS data from a dataset, exploring how to interpret neural state and how to update the policy to align with human intent outside of a real-time loop. The focus is how to reliably use those signals to update the policy of a robotic or artificial agent from offline data.

Papers

• IROS 2026 workshop paper
• Full paper under review for 2027. Link TBA.