# DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

arXiv: https://arxiv.org/abs/1909.13003 · HTML: https://mlsanity.com/paper/1909.13003/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2019.

## Built on by (most cited first, 21 of 21)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [Structured World Belief for Reinforcement Learning in POMDP](https://mlsanity.com/paper/2107.08577/) (2021)
- [Critic Sequential Monte Carlo](https://mlsanity.com/paper/2205.15460/) (2022)
- [DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation](https://mlsanity.com/paper/2403.09900/) (2024)
- [VL-TGS: Trajectory Generation and Selection using Vision Language Models in Mapless Outdoor Environments](https://mlsanity.com/paper/2408.02454/) (2024)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [Compositional Learning-based Planning for Vision POMDPs](https://mlsanity.com/paper/2112.09456/) (2021)
- [Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation](https://mlsanity.com/paper/2109.11288/) (2021)
- [SocialNav-MoE: A Mixture-of-Experts Vision Language Model for Socially Compliant Navigation with Reinforcement Fine-Tuning](https://mlsanity.com/paper/2512.14757/) (2025)
- [Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess](https://mlsanity.com/paper/2110.01810/) (2021)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [Sequential Monte Carlo for Policy Optimization in Continuous POMDPs](https://mlsanity.com/paper/2505.16732/) (2025)
- [Mixed Strategy Nash Equilibrium for Crowd Navigation](https://mlsanity.com/paper/2403.01537/) (2024)
- [Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice](https://mlsanity.com/paper/2311.07745/) (2023)
- [VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning](https://mlsanity.com/paper/2309.07832/) (2023)
- [DnD Filter: Differentiable State Estimation for Dynamic Systems using Diffusion Models](https://mlsanity.com/paper/2503.01274/) (2025)
- [Perception-Based Beliefs for POMDPs with Visual Observations](https://mlsanity.com/paper/2602.05679/) (2026)
- [Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds](https://mlsanity.com/paper/2603.06729/) (2026)
- [MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation](https://mlsanity.com/paper/2209.09079/) (2022)
- [Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners](https://mlsanity.com/paper/2410.12232/) (2024)

## Abstract (arXiv)

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network. In particular, we first introduce an adversarial particle filter that leverages the adversarial relationship between its internal components. Based on the filtering results, we then propose a planning algorithm that extends the previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex observations such as image input but also it remains highly interpretable. It is shown to be effective in three continuous POMDP domains: the floor positioning domain, the 3D light-dark navigation domain, and a modified Reacher domain.