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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66984
Volume:
10 |
8 |
Aug 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Dept of Computer Science and Engineering
Assam Engineering College
As humanoid robots transition from isolated industrial settings to shared human environments—including healthcare, domestic assistance, and disaster response—their decision-making systems must become transparent and verifiable. While conventional Explainable Artificial Intelligence (XAI) focuses on static digital data, Embodied XAI addresses the challenge of interpreting complex, non-linear deep neural networks controlling multi-degree-of-freedom physical agents. This paper establishes a formal framework for humanoid interpretability, distinguishing between cognitive intention mapping and kinematic post-hoc attribution. We analyze how standard XAI methodologies, such as SHAP and LIME, can be adapted to explain physical actuator outputs, sensorimotor loops, and trajectory planning. Furthermore, we introduce the concept of "Kinematic Recourse," demonvising how counterfactual explanations can be physically manifested through alternative robotic gestures. Finally, we map the performance-interpretability frontier unique to real-time physical systems, highlighting the computational latencies and safety trade-offs inherent in deploying explainability frameworks on embodied humanoid platforms.
KEYWORDS
Embodied XAI is an absolute prerequisite for the safe, transparent deployment of humanoid robots in human-centric spaces. Transforming black-box neural controllers into legible physical agents requires a tight coupling of real-time post-hoc attribution, natural language explanations, and expressive kinematic design. Future research must focus on optimizing E-XAI algorithms to run efficiently within sub-millisecond robotic control loops without compromising physical safety or explanation accuracy.
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Publication Date
8/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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