C-Ray is an amphibious robot that is capable of swimming in water and crawling on land using its undulating fins, enabling operations in a wide range of environments. The robot can be modeled as a hybrid dynamical system whose dynamics and propulsion change when the robot transitions between water and land. Most importantly, the direction of wave travel in the robot's fins is reversed between its swimming and crawling locomotion styles. To operate autonomously, C-Ray requires both accurate identification of when transitions between water and land occur and robust state estimation in littoral environments where the transition dynamics are highly discontinuous and transient. This paper presents a hybrid observer for estimating continuous states and identifying state-driven mode switches for C-Ray, enabling autonomous water/land-transitions. The proposed observer is a combination of the multiplicative extended Kalman filter (MEKF) and the salted Kalman filter, a newly proposed Kalman filter for mapping state uncertainty during hybrid transitions. We also propose an altitude and sea floor geometry observer and incorporate this directly into the MEKF. The performance is evaluated in simulations.

This work presents a study on how to exploit the CLIP embedding space to perform Visual Sentiment Analysis. We experiment with two architectures built on top of the CLIP embedding space, which we denote by CLIP-E. We train the CLIP-E models with WEBEmo, the largest publicly available and manually labeled benchmark for Visual Sentiment Analysis, and perform two sets of experiments. First, we test on WEBEmo and compare the CLIP-E architectures with state-of-the-art (SOTA) models and with CLIP Zero-Shot. Second, we perform cross dataset evaluation, and test the CLIP-E architectures trained with WEBEmo on other Visual Sentiment Analysis benchmarks. Our results show that the CLIP-E approaches outperform SOTA models in WEBEmo fine grained categorization, and they also generalize better when tested on datasets that have not been seen during training. Interestingly, we observed that for the FI dataset, CLIP Zero-Shot produces better accuracies than SOTA models and CLIP-E trained on WEBEmo. These results motivate several questions that we discuss in this paper, such as how we should design new benchmarks and evaluate Visual Sentiment Analysis, and whether we should keep designing tailored Deep Learning models for Visual Sentiment Analysis or focus our efforts on better using the knowledge encoded in large vision-language models such as CLIP for this task. Our code is available at https://github.com/cristinabustos16/CLIP-E.