Kalvik Jakkala

Kalvik Jakkala, Saurav Agarwal, Jason O'Kane et al.

Environmental monitoring robots often need to estimate data fields (e.g., salinity, temperature, bathymetry) under tight resource constraints. Classical boustrophedon lawnmower surveys provide geometric coverage guarantees but can waste effort by oversampling predictable regions. In contrast, informative path planning (IPP) methods leverage spatial correlations to reduce oversampling, yet typically offer no guarantees on estimation quality. This paper bridges these approaches by addressing IPP with guaranteed estimation uncertainty in complex environments: computing the shortest path whose measurements ensure that the Gaussian process (GP) posterior variance -- an intrinsic uncertainty measure that lower-bounds the mean-squared prediction error under the GP model -- is upper bounded by a user-specified threshold over the monitoring region. We propose a three-stage approach for efficient environmental monitoring: (i) learning a GP model from prior information; (ii) transforming the GP kernel into binary coverage maps that identify locations where uncertainty can be reduced below a target threshold; and (iii) planning a near-shortest route to satisfy the global uncertainty constraint. Our approach incorporates non-stationary kernels to capture spatially varying correlations in heterogeneous phenomena and accommodates non-convex environments with obstacles. We provide near-optimal approximation guarantees for both sensing-location selection and the joint selection-and-routing problem under a travel budget. Experiments on real-world topographic data demonstrate that our planners achieve uncertainty targets with fewer sensing locations and shorter travel distances than representative baselines. Furthermore, field experiments with autonomous surface and underwater vehicles validate the real-world feasibility of the approach. Our code is available at: www.sgp-tools.com

Ekkasit Pinyoanuntapong, Ayman Ali, Kalvik Jakkala et al.

mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy protection and is resilient to weather and lighting conditions. However, its generalization performance is yet unknown and limits its practical deployment. To address this problem, in this paper, a non-synthetic dataset is collected and analyzed to reveal the presence of spatial and temporal domain shifts in mmWave gait biometric data, which significantly impacts identification accuracy. To mitigate this issue, a novel self-aligned domain adaptation method called GaitSADA is proposed. GaitSADA improves system generalization performance by using a two-stage semi-supervised model training approach. The first stage employs semi-supervised contrastive learning to learn a compact gait representation from both source and target domain data, aligning source-target domain distributions implicitly. The second stage uses semi-supervised consistency training with centroid alignment to further close source-target domain gap by pseudo-labelling the target-domain samples, clustering together the samples belonging to the same class but from different domains, and pushing the class centroid close to the weight vector of each class. Experiments show that GaitSADA outperforms representative domain adaptation methods with an improvement ranging from 15.41\% to 26.32\% on average accuracy in low data regimes. Code and dataset will be available at https://exitudio.github.io/GaitSADA

Kalvik Jakkala, Srinivas Akella

The sensor placement problem is a common problem that arises when monitoring correlated phenomena, such as temperature, precipitation, and salinity. Existing approaches to this problem typically formulate it as the maximization of information metrics, such as mutual information~(MI), and use optimization methods such as greedy algorithms in discrete domains, and derivative-free optimization methods such as genetic algorithms in continuous domains. However, computing MI for sensor placement requires discretizing the environment, and its computation cost depends on the size of the discretized environment. These limitations restrict these approaches from scaling to large problems. We present a novel formulation to the SP problem based on variational approximation that can be optimized using gradient descent, allowing us to efficiently find solutions in continuous domains. We generalize our method to also handle discrete environments. Our experimental results on four real-world datasets demonstrate that our approach generates sensor placements consistently on par with or better than the prior state-of-the-art approaches in terms of both MI and reconstruction quality, all while being significantly faster. Our computationally efficient approach enables both large-scale sensor placement and fast robotic sensor placement for informative path planning algorithms.

Kalvik Jakkala

Gaussian processes are one of the dominant approaches in Bayesian learning. Although the approach has been applied to numerous problems with great success, it has a few fundamental limitations. Multiple methods in literature have addressed these limitations. However, there has not been a comprehensive survey of the topics as of yet. Most existing surveys focus on only one particular variant of Gaussian processes and their derivatives. This survey details the core motivations for using Gaussian processes, their mathematical formulations, limitations, and research themes that have flourished over the years to address said limitations. Furthermore, one particular research area is Deep Gaussian Processes (DGPs), it has improved substantially in the past decade. The significant publications that advanced the forefront of this research area are outlined in their survey. Finally, a brief discussion on open problems and research directions for future work is presented at the end.

Kalvik Jakkala, Arupjyoti Bhuya, Zhi Sun et al.

Gait is a person's natural walking style and a complex biological process that is unique to each person. Recently, the channel state information (CSI) of WiFi devices have been exploited to capture human gait biometrics for user identification. However, the performance of existing CSI-based gait identification systems is far from satisfactory. They can only achieve limited identification accuracy (maximum $93\%$) only for a very small group of people (i.e., between 2 to 10). To address such challenge, an end-to-end deep CSI learning system is developed, which exploits deep neural networks to automatically learn the salient gait features in CSI data that are discriminative enough to distinguish different people Firstly, the raw CSI data are sanitized through window-based denoising, mean centering and normalization. The sanitized data is then passed to a residual deep convolutional neural network (DCNN), which automatically extracts the hierarchical features of gait-signatures embedded in the CSI data. Finally, a softmax classifier utilizes the extracted features to make the final prediction about the identity of the user. In a typical indoor environment, a top-1 accuracy of $97.12 \pm 1.13\%$ is achieved for a dataset of 30 people.