1 citations · 1 across the 9 of their papers we have counts for
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Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning
Anutam Srinivasan, Siddhant Panse, Glen Chou
Robust adaptive control is vital for keeping robots safe while actively reducing uncertainty in unfamiliar settings. However, traditional methods are often conservative since they…
GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics
Jeffrey Fang, Keyi Shen, Anutam Srinivasan +1
This paper studies real-time robust optimal control for uncertain nonlinear systems, where linear time-varying (LTV) approximations make planning tractable but require sound linear…
Towards Auditing AI Systems in the Wild
Aditya T. Vadlamani, Anutam Srinivasan, Srinivasan Parthasarathy
AI systems are increasingly deployed in real-world settings where their behavior is shaped by dynamic environments, evolving data distributions, and complex interactions with users…
Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC
Devesh Nath, Anutam Srinivasan, Haoran Yin +3
We present SLS^2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC) in learned latent world models. Our approach trains an actio…
Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
Anutam Srinivasan, Antoine Leeman, Glen Chou
We present a novel framework for robust out-of-distribution planning and control using conformal prediction (CP) and system level synthesis (SLS), addressing the challenge of ensur…