# Diffusion Policy with Bayesian Expert Selection for Active Multi-Target Tracking

arXiv: https://arxiv.org/abs/2604.03404 · HTML: https://mlsanity.com/paper/2604.03404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [Variational Bayesian Last Layers](https://mlsanity.com/paper/2404.11599/) (2024)
- [Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning](https://mlsanity.com/paper/2407.01531/) (2024)
- [Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts](https://mlsanity.com/paper/2311.11385/) (2023)
- [Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning](https://mlsanity.com/paper/2412.12953/) (2024)
- [Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization](https://mlsanity.com/paper/2111.13807/) (2021)
- [HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots](https://mlsanity.com/paper/1903.09845/) (2019)
- [Variational Distillation of Diffusion Policies into Mixture of Experts](https://mlsanity.com/paper/2406.12538/) (2024)
- [Bayesian Optimization via Continual Variational Last Layer Training](https://mlsanity.com/paper/2412.09477/) (2024)
- [DARE: Diffusion Policy for Autonomous Robot Exploration](https://mlsanity.com/paper/2410.16687/) (2024)
- [Resilient Active Target Tracking with Multiple Robots](https://mlsanity.com/paper/1809.04032/) (2018)
- [An Uncertainty-Aware Performance Measure for Multi-Object Tracking](https://mlsanity.com/paper/2108.04619/) (2021)
- [An Informative Planning Framework for Target Tracking and Active Mapping in Dynamic Environments with ASVs](https://mlsanity.com/paper/2508.14636/) (2025)
- [AID: Agent Intent from Diffusion for Multi-Agent Informative Path Planning](https://mlsanity.com/paper/2512.02535/) (2025)
- [Fine-tuning LLMs with variational Bayesian last layer for high-dimensional Bayesian optimization](https://mlsanity.com/paper/2510.01471/) (2025)
- [PMBM filter with partially grid-based birth model with applications in sensor management](https://mlsanity.com/paper/2103.10775/) (2021)
- [MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy](https://mlsanity.com/paper/2511.11931/) (2025)

## Abstract (arXiv)

Active multi-target tracking requires a mobile robot to balance exploration for undetected targets with exploitation of uncertain tracked ones. Diffusion policies have emerged as a powerful approach for capturing diverse behavioral strategies by learning action sequences from expert demonstrations. However, existing methods implicitly select among strategies through the denoising process, without uncertainty quantification over which strategy to execute. We formulate expert selection for diffusion policies as an offline contextual bandit problem and propose a Bayesian framework for pessimistic, uncertainty-aware strategy selection. A multi-head Variational Bayesian Last Layer (VBLL) model predicts the expected tracking performance of each expert strategy given the current belief state, providing both a point estimate and predictive uncertainty. Following the pessimism principle for offline decision-making, a Lower Confidence Bound (LCB) criterion then selects the expert whose worst-case predicted performance is best, avoiding overcommitment to experts with unreliable predictions. The selected expert conditions a diffusion policy to generate corresponding action sequences. Experiments on simulated indoor tracking scenarios demonstrate that our approach outperforms both the base diffusion policy and standard gating methods, including Mixture-of-Experts selection and deterministic regression baselines.