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Jaehong Oh

χPreprint · 2025

Towards Cognitive Collaborative Robots: Semantic-Level Integration and Explainable Control for Human-Centric Cooperation

Jaehong Oh

118 words1 min read

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Abstract

As the Fourth Industrial Revolution reshapes industrial paradigms, human-robot collaboration (HRC) has transitioned from a desirable capability to an operational necessity. In response, collaborative robots are evolving beyond repetitive tasks toward adaptive, semantically informed interaction with humans and environments. This paper surveys five foundational pillars enabling this transformation: semantic-level perception, cognitive action planning, explainable learning and control, safety-aware motion design, and multimodal human-intention recognition. We examine the role of semantic mapping in transforming spatial data into meaningful context, and explore cognitive planning frameworks that leverage this context for goal-driven decision-making. We analyze explainable reinforcement-learning methods — including policy distillation and attention mechanisms — which enhance interpretability and trust. Safety is addressed through force-adaptive control and risk-aware trajectory planning; seamless human interaction is supported via gaze- and gesture-based intent recognition. To address the remaining challenges, we propose a unified Cognitive Synergy Architecture integrating all modules into a cohesive framework for truly human-centric cobot collaboration.

Overview

A review article surveying the five pillars of cognitive collaborative robotics and proposing a unified Cognitive Synergy Architecture as a roadmap. The review is explicitly a preprint — not yet peer-reviewed at the time of this listing.

The five pillars

  1. Semantic-level perception — transforming geometric maps into meaningful context.
  2. Cognitive action planning — goal-driven decision-making that leverages semantic context.
  3. Explainable learning and control — policy distillation, attention mechanisms, interpretable reinforcement learning.
  4. Safety-aware motion design — force-adaptive control and risk-aware trajectory planning.
  5. Multimodal human-intention recognition — gaze, gesture, and language channels fused into a single intent estimate.

Where it sits

Conceptual precursor to the SEGO architecture paper, which instantiates parts of the proposed architecture in code.

BibTeX· generated

@misc{oh2025cognitive,
  title   = {Towards Cognitive Collaborative Robots: Semantic-Level Integration and Explainable Control for Human-Centric Cooperation},
  author  = {Jaehong Oh},
  year    = {2025},
  url     = {https://jack0682.github.io/papers/cognitive-collaborative-robots/},
}