Harris, L. R. and Jenkin, M. Visual-vestibular integration in Challenging Environments. Cambridge Elements: Perception. Cambridge University Press, 2025.
This Element reviews the current state of what is known about the visual and vestibular contributions to our perception of self-motion and orientation with an emphasis on the central role that gravity plays in these perceptions. The Element then reviews the effects of impoverished challenging environments that do not provide full information that would normally contribute to these perceptions (such as driving a car or piloting an aircraft) and inconsistent challenging environments where expected information is absent, such as the microgravity experienced on the International Space Station.
Jilani, A., Hogan, F., Morissette, C., Dudek, G., Jenkin, M. and Siddiqi, K. Visual-tactile inference of 2.5D object shape from marker texture. IEEE Robotics and Automation Letters 10:1042-1049, 2025.
Visual-tactile sensing affords abundant capabilities for contact-rich object manipulation tasks including grasping and placing. Here we introduce a shape-from-texture inspired contact shape estimation approach for visual-tactile sensors equipped with visually distinct membrane markers. Under a perspective projection camera model, measurements related to the change in marker separation upon contact are used to recover surface shape. Our approach allows for shape sensing in real time, without requiring network training or complex assumptions related to lighting, sensor geometry or marker placement. Experiments show that the surface contact shape recovered is qualitatively and quantitatively consistent with those obtained through the use of photometric stereo, the current state of the art for shape recovery in visual-tactile sensors. Importantly, our approach is applicable to a large family of sensors not equipped with photometric stereo hardware, and also to those with semi-transparent membranes. The recovery of surface shape affords new capabilities to these sensors for robotic applications, such as the estimation of contact and slippage in object manipulation tasks (Hogan etal., 2022) and the use of force matching for kinesthetic teaching using multimodal visual-tactile sensing (Ablett etal., 2024)
Lin, W., Wu, D. and Jenkin, M. "Electric Load Forecasting for Individual Households via Spatial-Temporal Knowledge Distillation," in IEEE Transactions on Power Systems, vol. 40, no. 1, pp. 572-584, Jan. 2025
Short-term load forecasting (STLF) for residential households has become of critical importance for the secure operation of power grids as well as home energy management systems. While machine learning is effective for residential STLF, data and resource limitations hinder individual household predictions operated on local devices. In contrast, utility companies have access to broader sets of data as well as to better computational resources, and thus have the potential to deploy complex forecasting models such as Graph neural network-based models to explore the spatial-temporal relationships between households for achieving impressive STLF performance. In this work, we propose an efficient and privacy-conservative knowledge distillation-based STLF framework. This framework can improve the STLF forecasting accuracy of lightweight individual household forecasting models via leveraging the benefits of knowledge distillation and graph neural networks (GNN). Specifically, we distill the knowledge learned from a GNN model pre-trained on utility data sets into individual models without the need to access data sets of other households. Extensive experiments on real-world residential electric load datasets demonstrate the effectiveness of the proposed method.
