Particle Filtering with Generative Trajectory Models for State Estimation and Control
Overview
This research paper introduces a method that combines particle filtering with generative trajectory models to enhance state estimation and control in robotics. The authors propose using a single pretrained diffusion model to generate competing hypotheses about state histories, along with predicted observations and action proposals. By reweighting particles based on how well their predicted observations match new measurements, the system improves both state estimation accuracy and task performance. Simulation experiments in partially observed navigation and vision-based manipulation show that this approach outperforms standard action generation methods using the same pretrained model without requiring additional test-time information or model modifications.
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“according to how well their predicted observations match the measurements. While particle filtering conventionally uses these weights only for state estimation, we also use them to guide action selection, favoring proposals associated with hypotheses that better explain the observations. Simulation experiments in partially observed navigation and vision-based manipulation demonstrate improvements in both state estim”
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- arXiv Robotics — research abstractsSignalParticle Filtering with Generative Trajectory Models for State Estimation and Control
This research paper introduces a method that combines particle filtering with generative trajectory models to enhance state estimation and control in robotics. The authors propose using a single pretrained diffusion model to generate competing hypotheses about state histories, along with predicted observations and action proposals. By reweighting particles based on how well their predicted observations match new measurements, the system improves both state estimation accuracy and task performance. Simulation experiments in partially observed navigation and vision-based manipulation show that this approach outperforms standard action generation methods using the same pretrained model without requiring additional test-time information or model modifications.
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