Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing
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| Format: | Preprint |
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2025
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| _version_ | 1866914008233148416 |
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| author | Puthumanaillam, Gokul Penumarti, Aditya Vora, Manav Padrao, Paulo Fuentes, Jose Bobadilla, Leonardo Shin, Jane Ornik, Melkior |
| author_facet | Puthumanaillam, Gokul Penumarti, Aditya Vora, Manav Padrao, Paulo Fuentes, Jose Bobadilla, Leonardo Shin, Jane Ornik, Melkior |
| contents | Robots equipped with rich sensor suites can localize reliably in partially-observable environments, but powering every sensor continuously is wasteful and often infeasible. Belief-space planners address this by propagating pose-belief covariance through analytic models and switching sensors heuristically--a brittle, runtime-expensive approach. Data-driven approaches--including diffusion models--learn multi-modal trajectories from demonstrations, but presuppose an accurate, always-on state estimate. We address the largely open problem: for a given task in a mapped environment, which \textit{minimal sensor subset} must be active at each location to maintain state uncertainty \textit{just low enough} to complete the task? Our key insight is that when a diffusion planner is explicitly conditioned on a pose-belief raster and a sensor mask, the spread of its denoising trajectories yields a calibrated, differentiable proxy for the expected localisation error. Building on this insight, we present Belief-Conditioned One-Step Diffusion (B-COD), the first planner that, in a 10 ms forward pass, returns a short-horizon trajectory, per-waypoint aleatoric variances, and a proxy for localisation error--eliminating external covariance rollouts. We show that this single proxy suffices for a soft-actor-critic to choose sensors online, optimising energy while bounding pose-covariance growth. We deploy B-COD in real-time marine trials on an unmanned surface vehicle and show that it reduces sensing energy consumption while matching the goal-reach performance of an always-on baseline. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_12166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing Puthumanaillam, Gokul Penumarti, Aditya Vora, Manav Padrao, Paulo Fuentes, Jose Bobadilla, Leonardo Shin, Jane Ornik, Melkior Robotics Machine Learning Systems and Control Robots equipped with rich sensor suites can localize reliably in partially-observable environments, but powering every sensor continuously is wasteful and often infeasible. Belief-space planners address this by propagating pose-belief covariance through analytic models and switching sensors heuristically--a brittle, runtime-expensive approach. Data-driven approaches--including diffusion models--learn multi-modal trajectories from demonstrations, but presuppose an accurate, always-on state estimate. We address the largely open problem: for a given task in a mapped environment, which \textit{minimal sensor subset} must be active at each location to maintain state uncertainty \textit{just low enough} to complete the task? Our key insight is that when a diffusion planner is explicitly conditioned on a pose-belief raster and a sensor mask, the spread of its denoising trajectories yields a calibrated, differentiable proxy for the expected localisation error. Building on this insight, we present Belief-Conditioned One-Step Diffusion (B-COD), the first planner that, in a 10 ms forward pass, returns a short-horizon trajectory, per-waypoint aleatoric variances, and a proxy for localisation error--eliminating external covariance rollouts. We show that this single proxy suffices for a soft-actor-critic to choose sensors online, optimising energy while bounding pose-covariance growth. We deploy B-COD in real-time marine trials on an unmanned surface vehicle and show that it reduces sensing energy consumption while matching the goal-reach performance of an always-on baseline. |
| title | Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing |
| topic | Robotics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2508.12166 |