SmokeBot: A Perception-to-Manipulation Framework and Simulation Platform for Robotic Manipulation in Smoke Scenes

Robotic manipulation in smoke scenes is common yet highly challenging, as airborne scattering media degrade RGB observations and make active depth sensing unreliable. Existing robotic manipulation systems are primarily optimized for unobscured visual conditions, yet they overlook the complex multi-modal degradation induced by dynamic and spatially non-uniform smoke, which can severely impair perceptual reliability and thereby lead to a significant drop in manipulation performance. To address this challenge, we propose SmokeManip, a perception-to-manipulation framework for smoke-robust robotic manipulation without relying on active depth sensors. It decouples robotic perception into a scene understanding model and an affordance prediction model, both trained with geometric supervision to enable adaptive geometric and visual feature modeling under smoke. The former parses the task scene and localizes target objects, conditioning the latter to predict object-centric affordances that generate task-aware and physically feasible end-effector poses. For manipulation, SmokeManip further leverages a reusable action primitive library and hierarchical closed-loop recovery mechanism to compensate for perception inaccuracies and maintain stable manipulation. Beyond the algorithmic challenges, smoke-scene data collection and robotic manipulation evaluation lack a suitable platform. Therefore, we further construct SmokeLab, a dedicated simulation platform for robotic manipulation in smoke scenes. SmokeLab provides high-fidelity dynamic smoke simulation, automated data collection, and unified evaluation protocols for robotic perception and manipulation. Extensive experiments in SmokeLab and real-world demonstrate that our approach outperforms existing baselines across varying smoke densities. The project page is available at [https://smokebot-web.github.io/](https://smokebot-web.github.io/).