State Inference

State inference focuses on estimating the underlying state of a system based on incomplete or noisy observations, a crucial task across diverse fields. Current research emphasizes improving the efficiency and accuracy of inference, particularly in high-dimensional and dynamic systems, employing techniques like particle filters, diffusion models, and variational autoencoders, often within deep learning frameworks. These advancements are driving progress in areas such as robotics, autonomous navigation, and multi-agent systems, where accurate state estimation is essential for effective decision-making and control. Furthermore, research is exploring methods to automate model comparison and selection for improved state inference.

Papers

August 18, 2024

Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Hangyu Mao, Nianmin Zhang, Xin Xin, Pengjie Ren, Dapeng Li, Bin Zhang, Guoliang Fan, Zhumin Chen, Changwei Wang, Jiangjin Yin
Diffusion Model Multi Agent Reinforcement Learning Cooperative Multi Agent Reinforcement Learning Multi Agent Setting State Inference Multi Agent Reinforcement Learning Environment

February 26, 2024

Differentiable Particle Filtering using Optimal Placement Resampling
Domonkos Csuzdi, Olivér Törő, Tamás Bécsi
Parameter Estimation Particle Filter Differentiable Particle Filter Gaussian State Space Model Optimal Placement State Inference

February 6, 2024

Adaptive Inference: Theoretical Limits and Unexplored Opportunities
Soheil Hor, Ying Qian, Mert Pilanci, Amin Arbabian
High Efficiency Fundamental Limit Adaptive Inference State Inference

November 28, 2023

On the Impact of Sampling on Deep Sequential State Estimation
Helena Calatrava, Ricardo Augusto Borsoi, Tales Imbiriba, Pau Closas
Global Impact Open Sampling Dynamical Variational State Inference Deep Kalman Filter

November 27, 2023

Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Jiachen Li, David Isele, Kanghoon Lee, Jinkyoo Park, Kikuo Fujimura, Mykel J. Kochenderfer
Deep Reinforcement Learning Autonomous Vehicle Spatio Temporal Autonomous Navigation State Inference

June 9, 2023

Automating Model Comparison in Factor Graphs
Bart van Erp, Wouter W. L. Nuijten, Thijs van de Laar, Bert de Vries
Factor Graph Model Comparison Temporal Prior Probabilistic Programming Language Bayesian Model Averaging Probabilistic State State Inference

March 3, 2023

Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework
Shageenderan Sapai, Junn Yong Loo, Ze Yang Ding, Chee Pin Tan, Raphael CW Phan, Vishnu Monn Baskaran, Surya Girinatha Nurzaman
Transfer Learning Soft Robot Soft Robotic Hand State Inference

December 29, 2021

Efficient Belief Space Planning in High-Dimensional State Spaces using PIVOT: Predictive Incremental Variable Ordering Tactic
Khen Elimelech, Vadim Indelman
Belief State Dimensional State Space APT Tactic Active SLAM Belief Space Planning State Inference