Cognitive Computing Frameworks for Adaptive Human-Robot Collaboration

Abstract

Collaborative robots increasingly share a workspace, a task, and even a single object with human partners, a shift that requires
software capable of representing what a human collaborator is doing, intending, and about to need rather than treating the
human as just another obstacle. This article synthesizes the literature on the cognitive computing frameworks built to meet
that requirement: symbolic cognitive architectures such as Soar and ACT-R, and their embodiment in physical robots through
extensions like ACT-R/E, that supply a representational backbone for robot reasoning; perception and intention-recognition
pipelines that have moved from rule-based heuristics to deep sequence models reporting high accuracy on constrained
benchmarks; planning and decision-making layers that treat human trust, intention, and preference as evolving hidden state
rather than fixed input, often within formal frameworks such as partially observable Markov decision processes; and the
reinforcement-learning, learning-from-demonstration, and digital-twin methods that let all of the above keep adapting after
deployment. Read together, the evidence supports cognitive computing as a coherent and increasingly well-evidenced
architectural pattern for adaptive human-robot collaboration, while leaving evaluation at realistic deployment scale, two-way
legibility between robot and human, and integration of symbolic and learned reasoning as a substantially open research
agenda.