Hybrid Vision and Language Models for Robotics and Human Machine Interaction
Download PDFAbstract
This chapter explores the cutting-edge intersection of computer vision, natural language processing, and robotics, focusing on the development and application of hybrid vision and language models (VLMs) for enhanced human-machine interaction (HMI).We delve into the architectural evolution of these models, from early unimodal systems to sophisticated, multimodal frameworks that enable robots to perceive, reason, and act in ncomplex, dynamic environments. The chapter presents a comprehensive review of the literature, highlighting key advancements in visionlanguage-action (VLA) models and their impact on robotics. We then propose a novel hybrid methodology that synergizes the strengths of different VLM architectures to improve robotic manipulation and HMI. A detailed discussion of experimental results on a challenging manipulation task benchmark demonstrates the efficacy of the proposed approach. The chapter concludes with a summaryof key findings, a discussion of current challenges and limitations, and an outlook on future research directions in this rapidly evolving field.
References
- K Schwab. “The Fourth Industrial Revolution, Crown Business, New York”. In: The smart-up ecosystem: Turning Open Innovation into smart business (2017).
- Ane Bl´azquez-Garc´ıa et al. “A review on outlier/anomaly detection in time serie data”. In: ACM computing surveys (CSUR) 54.3 (2021), pp. 1–33.
- Varun Chandola, Arindam Banerjee, and Vipin Kumar. “Anomaly detection: A survey”. In: ACM computing surveys (CSUR) 41.3 (2009), pp. 1–58.
- Diederik P Kingma and Max Welling. “Auto-encoding variational bayes”. In: arXiv preprint arXiv:1312.6114 (2013).
- Douglas C Montgomery. Introduction to statistical quality control. John wiley & sons, 2020.
- Martin Ester et al. “A density-based algorithm for discovering clusters in large spatial databases with noise”. In: kdd. Vol. 96. 34. 1996, pp. 226–231.
- Bernhard Sch¨olkopf et al. “Estimating the support of a high-dimensional distribution”. In: Neural computation 13.7 (2001), pp. 1443–1471.
- Mayu Sakurada and Takehisa Yairi. “Anomaly detection using autoencoders with nonlinear dimensionality reduction”. In: Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis. 2014, pp. 4–11.
- Jinwon An and Sungzoon Cho. “Variational autoencoder based anomaly detection using reconstruction probability”. In: Special lecture on IE 2.1 (2015), pp. 1–18.
- Pankaj Malhotra et al. “Long short term memory networks for anomaly detection in time series”. In: Proceedings. Vol. 89. 9. 2015, p. 94.
Principles of Hybrid Intelligent Systems
