4 citations · 7 across the 9 of their papers we have counts for
10 papers
VCTP: Vehicle-Conditioned Terrain Planning for Off-Road Navigation
Akshay Naik, Ramavarapu S. Sreenivas, Dustin Nottage +1
A vehicle's heading affects both the surfaces beneath its tires and its pitch and roll. We present Vehicle-Conditioned Terrain Planning (VCTP), which retains these relationships by…
Underwater Dead Reckoning with Deployable Situation-Triggered Covariance Scheduling
Akshay Naik, Ramavarapu S. Sreenivas, Dustin Nottage +1
Underwater dead reckoning estimates vehicle position when vision is unavailable and external positioning cannot be assumed. A single set of filter parameters can work well in many…
LDLT L-Lipschitz Network Weight Parameterization Initialization
Marius F. R. Juston, Ramavarapu S. Sreenivas, Dustin Nottage +1
We analyze initialization dynamics for LDLT-based -Lipschitz layers by deriving the exact marginal output variance when the underlying parameter matrix $W_0\in \mathbb…
LDLT -Lipschitz Network: Generalized Deep End-To-End Lipschitz Network Construction
Marius F. R. Juston, Ramavarapu S. Sreenivas, Dustin Nottage +1
Deep residual networks (ResNets) have demonstrated outstanding success in computer vision tasks, attributed to their ability to maintain gradient flow through deep architectures. S…
Safety Monitor for Off-Road Planning with Uncertainty Bounded Bekker Costs
Akshay Naik, Ramavarapu S. Sreenivas, William R. Norris +3
Reliable off-road autonomy requires operational constraints so that behavior stays predictable and safe when soil strength is uncertain. This paper presents a runtime assurance saf…
Path Enumeration by Position-Visit Counts in Recombining Trinomial Trees
Ethan Torres, Ramavarapu Sreenivas, Richard Sowers
Recombining trinomial trees are a workhorse for modeling discrete-event systems in option pricing, logistics, and feedback control. Because each node stores a state-dependent quant…
Neuro-Symbolic Generation of Explanations for Robot Policies with Weighted Signal Temporal Logic
Mikihisa Yuasa, Ramavarapu S. Sreenivas, Huy T. Tran
Neural network-based policies have demonstrated success in many robotic applications, but often lack human-explanability, which poses challenges in safety-critical deployments. To…
On Generating Explanations for Reinforcement Learning Policies: An Empirical Study
Mikihisa Yuasa, Huy T. Tran, Ramavarapu S. Sreenivas
Understanding a \textit{reinforcement learning} policy, which guides state-to-action mappings to maximize rewards, necessitates an accompanying explanation for human comprehension.…
Comparison of Spatiotemporal Networks for Learning Video Related Tasks
Logan Courtney, Ramavarapu Sreenivas
Many methods for learning from video sequences involve temporally processing 2D CNN features from the individual frames or directly utilizing 3D convolutions within high-performing…
Learning from Videos with Deep Convolutional LSTM Networks
Logan Courtney, Ramavarapu Sreenivas
This paper explores the use of convolution LSTMs to simultaneously learn spatial- and temporal-information in videos. A deep network of convolutional LSTMs allows the model to acce…