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Paper ID:IJSREM66984

Embodied Explainable AI: Interpretability Frameworks for Humanoid Robot Decision-Making and Kinematic Recourse

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM66984

Volume:

10 |

8 |

Aug 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM66984

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.

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Authors

DGK

Dr Gunajit Kalita

Dept of Computer Science and Engineering

Assam Engineering College

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Abstract

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 Humanoid Robotics Kinematic Recourse Sensorimotor Attribution Human-Robot Interaction (HRI)

Cite This Article

Dr Gunajit Kalita, "Embodied Explainable AI: Interpretability Frameworks for Humanoid Robot Decision-Making and Kinematic Recourse," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 8, pp. 1-5, Aug 2026. https://doi.org/10.55041/IJSREM66984

Full Article/Conclusion

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.

References

[1] O. Khatib, "Real-time obstacle avoidance for manipulators and mobile robots," Int. J. Robotics Res., vol. 5, no. 1, pp. 90–98, 1986. [2] A. Dragan, K. Lee, and S. Srinivasa, "Legibility and predictability of robot motion," Proc. ACM/IEEE Int. Conf. Human-Robot Interaction (HRI), 2013, pp. 301–308. [3] M. T. Ribeiro, S. Singh, and C. Guestrin, "'Why should I trust you?': Explaining the predictions of any classifier," in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 1135–1144. [4] R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi, "A survey of methods for explaining black box models," ACM Comput. Surv., vol. 51, no. 5, pp. 1–42, 2018. [5] C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2nd ed. Munich, Germany: Leanpub, 2022. [6] S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 4765–4774. [7] S. Anca et al., "Modelling user expectations for human-robot interaction," Nature Machine Intelligence, vol. 2, no. 8, pp. 442–450, 2020. [8] S. Wachter, B. Mittelstadt, and C. Russell, "Counterfactual explanations without opening the black box: Automated decisions and the GDPR," Harvard Journal of Law & Technology, vol. 31, p. 841, 2018. [9] B. Kim et al., "Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)," in Proc. Int. Conf. Machine Learning (ICML), 2018, pp. 2668–2677. [10] D. Slack, S. Hilgard, E. Jia, S. Singh, and H. Lakkaraju, "Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods," in Proc. AAAI Conf. Human Computation and Crowdsourcing, 2020, pp. 180–186.

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Publication Date

8/27/2026

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Double-Blind Peer Review

Review Rounds: 2

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