13 citations · 13 across the 4 of their papers we have counts for
6 papers
LEMCA: LLM-Guided Synthesis of Efficient Mode-Switching Control Architectures
Arjun Krishna, Vincent Pacelli, Dinesh Jayaraman
Physical control tasks in the natural world, such as driving or object manipulation, frequently exhibit dramatic variations in sensory and compute complexity over time. Correspondi…
Fundamental Limits for Sensor-Based Robot Control
Anirudha Majumdar, Zhiting Mei, Vincent Pacelli
Our goal is to develop theory and algorithms for establishing fundamental limits on performance imposed by a robot's sensors for a given task. In order to achieve this, we define a…
Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy
Vincent Pacelli, Anirudha Majumdar
The rapid development of affordable and compact high-fidelity sensors (e.g., cameras and LIDAR) allows robots to construct detailed estimates of their states and environments. Howe…
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
Anoopkumar Sonar, Vincent Pacelli, Anirudha Majumdar
A fundamental challenge in reinforcement learning is to learn policies that generalize beyond the operating domains experienced during training. In this paper, we approach this cha…
Learning Task-Driven Control Policies via Information Bottlenecks
Vincent Pacelli, Anirudha Majumdar
This paper presents a reinforcement learning approach to synthesizing task-driven control policies for robotic systems equipped with rich sensory modalities (e.g., vision or depth)…
Task-Driven Estimation and Control via Information Bottlenecks
Vincent Pacelli, Anirudha Majumdar
Our goal is to develop a principled and general algorithmic framework for task-driven estimation and control for robotic systems. State-of-the-art approaches for controlling roboti…