Aditya Narayanan, Pranav Kasibhatla, Minkyu Choi, Po-han Li, Ruihan Zhao, Sandeep Chinchali
International Conference on Intelligent Robots and Systems (IROS), 2024.

This post introduces PEERNet, a Python package for real-time benchmarking of networked robotic systems. It provides concise and modular methods for performance analysis of the entire system stack.

Recent advances have enabled diffusion models to efficiently compress videos while maintaining high visual quality. By storing only keyframes and using these models to interpolate frames during playback, this method ensures high fidelity with minimal data. The process is adaptive, balancing detail retention and compression ratio, and can be conditioned on lightweight information like text descriptions or edge maps for improved results.

Distill series – diffusion model.

Minkyu Choi, Harsh Goel, Mohammad Omama, Yunhao, Yang, Sahil Shah, and Sandeep Chinchali
European Conference on Computer Vision (ECCV), 2024 – Accepted for oral presentation!

Thayne T. Walker, Jaime S. Ide, Minkyu Choi, Michael John Guarino, and Kevin Alcedo.
International Conference on Control, Decision and Information Technologies (CoDit), 2023

The coordination of multiple autonomous agents is essential for achieving collaborative goals efficiently, especially in environments with limited communication and sensing capabilities. Our recent study, presented at CoDIT 2023, explores a novel method to tackle this challenge. We introduce Multi-Agent Reinforcement Learning with Epistemic Priors (MARL-EP), a technique that leverages shared mental models to enable high-level coordination among agents, even with severely impaired sensing and zero communication.

Minkyu Choi, Max Filter, Kevin, Alcedo, Thayne T. Walker, David Rosenbluth, and Jaime S. Ide.
International Conference on Unmanned Aircraft Systems (ICUAS), 2022

The rapid evolution in autonomous unmanned aerial vehicles (UAVs) technology has spurred significant advancements in their control systems. A prominent challenge in this domain is balancing the agility of Proportional-Integral-Derivative (PID) systems for low-level control with the adaptability of Deep Reinforcement Learning (DRL) for navigation through complex environments. This post delves into a novel approach that combines these technologies to improve the retraining efficiency of UAV controllers using Soft Actor-Critic (SAC) with inhibitory networks.