2 citations · 2 across the 9 of their papers we have counts for
10 papers
Structured LLM Reasoning for Zero-Shot Human--Robot Coordination Under Hidden Goals
Dong Hae Mangalindan, Anand Gokhale, Francesco Bullo +1
We present a structured large-language-model (LLM) architecture for zero-shot human--robot coordination in a cooperative construction task with private goal views. Guided by a Dec-…
Regularized Model Predictive Control via Contractivity and Implicit Lur'e Analysis
Ryotaro Shima, Anand Gokhale, Alexander Davydov +1
This paper develops a contraction-based stability analysis for regularized model predictive control (MPC), whose feedback law is defined implicitly by a finite-horizon optimal cont…
A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
Anand Gokhale, Anton V. Proskurnikov, Yu Kawano +1
This paper establishes a nonlinear separation principle based on contraction theory and derives sharp stability conditions for recurrent neural networks (RNNs). First, we introduce…
Contracting Neural Networks: Sharp LMI Conditions with Applications to Integral Control and Deep Learning
Anand Gokhale, Anton V. Proskurnikov, Yu Kawano +1
This paper studies contractivity of firing-rate and Hopfield recurrent neural networks. We derive sharp LMI conditions on the synaptic matrices that characterize contractivity of b…
LogicGuard: Improving Embodied LLM agents through Temporal Logic based Critics
Anand Gokhale, Vaibhav Srivastava, Francesco Bullo
Large language models (LLMs) have shown promise in zero-shot and single step reasoning and decision making problems, but in long horizon sequential planning tasks, their errors com…
Learning to Lie: Reinforcement Learning Attacks Damage Human-AI Teams and Teams of LLMs
Abed Kareem Musaffar, Anand Gokhale, Sirui Zeng +4
As artificial intelligence (AI) assistants become more widely adopted in safety-critical domains, it becomes important to develop safeguards against potential failures or adversari…
Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications
Anand Gokhale, Alexander Davydov, Francesco Bullo
In this letter we study the proximal gradient dynamics. This recently-proposed continuous-time dynamics solves optimization problems whose cost functions are separable into a nonsm…
On Weakly Contracting Dynamics for Convex Optimization
Veronica Centorrino, Alexander Davydov, Anand Gokhale +2
We analyze the convergence behavior of \emph{globally weakly} and \emph{locally strongly contracting} dynamics. Such dynamics naturally arise in the context of convex optimization…
Positive Competitive Networks for Sparse Reconstruction
Veronica Centorrino, Anand Gokhale, Alexander Davydov +2
We propose and analyze a continuous-time firing-rate neural network, the positive firing-rate competitive network (\pfcn), to tackle sparse reconstruction problems with non-negativ…
Contractivity of Distributed Optimization and Nash Seeking Dynamics
Anand Gokhale, Alexander Davydov, Francesco Bullo
In this letter, we study distributed optimization and Nash equilibrium-seeking dynamics from a contraction theoretic perspective. Our first result is a novel bound on the logarithm…