A robot sent to a named gas leak must preserve gas identity, estimate the source, and navigate to the resulting goal. We present SmellDiffusion, a simulation pipeline that represents species-specific gas zones in an open-vocabulary olfactory scene graph and shares the selected goal between classical and diffusion planners. Its key components are a peak-local geometric gate for selective source correction and diffusion-based, gas-guided trajectory generation. Among 424 unique source-wind configurations in solved flow, 28 have a concentration peak displaced more than 0.5m from the source. A source-independent geometric gate, calibrated only on the training split and evaluated at the observed peak, detects 9 of 10 held-out displacements at 0.64 precision. Gating a precomputed forward-matching correction reduces mean error on the displaced cases from 1.468m to 0.592m (60%), using matching for only 14/204 cases. All-case mean error falls from 0.205m to 0.180m. All planners receive the same scene-graph source estimate as their goal. In a controlled comparison, best-of-ten diffusion achieves mean gas exposure comparable to gas-guided A* (0.0476 versus 0.0455). A single diffusion proposal takes 41.7ms, compared with 72.3ms for gas-guided A*, although best-of-ten sequential sampling increases total runtime. Plain A* also reaches the same goal and remains the fastest and shortest-path method. Six matched Gazebo runs give mean robot-to-source errors of 0.39m for A* and 0.31m for diffusion.
Many diffusion-based planners enforce safety through inference-time guidance, but such interleaved trajectory deformations often degrade kinematic feasibility due to manifold rupture. We propose Graph-Guided Safe Diffuser (G2SD), a hierarchical framework that leverages a high-level topological graph planner to guide a low-level diffusion model. G2SD enforces safety at a structural level by abstracting the data manifold into a learned latent graph, on which high-level planning is performed. Continuous trajectories are generated by diffusion planners, which are conditioned on the graph node representations selected by the high-level planner. Theoretical analyses demonstrate conditions under which manifold rupture occurs in diffusion planners, and show that G2SD improves safety by reducing the constraint violation probability as the number of segments increases. Experiments demonstrate that G2SD substantially outperforms baselines, increasing goal-reaching rate without any collision from 40-50% to 98% in Maze2D navigation and also achieving superior task scores in locomotion.
Olfaction offers robots access to chemical information that is largely inaccessible to vision, touch, and audition, yet using airborne chemical signals for spatial perception remains challenging because local volatile organic compound (VOC) measurements are shaped by complex chemical transport and sensor dynamics. We introduce Scensory, a robotic olfaction framework that learns to jointly infer biological source identity and relative location from short temporal VOC measurements. Using a robot-automated data collection platform, we pair VOC dynamics from cross-sensitive gas sensor arrays with spatial supervision and train models to predict fungal identity, source direction, and distance. We show that a single sensor array can extract all three quantities from only 3 s of local measurements under ambient environmental conditions, achieving species classification accuracy of up to 80.13%, directional accuracy of up to 68.65%, and mean absolute distance errors of 0.110-0.131 m. Incorporating measurements from multiple spatial locations further reduces ambiguity, improving peak species and directional accuracies by 9.72 and 18.66 percentage points, respectively. We then embody this learned olfactory perception on a mobile robot, where successive local predictions acquired during motion are transformed into a world-frame evidence map, allowing observations from different positions and headings to reinforce persistent source hypotheses and guide closed-loop localization. Across eight selected indoor runs, the robot achieves a planar endpoint error of 0.606 +/- 0.294 m. Our results establish airborne chemical dynamics as a viable perceptual signal for robots to recognize biological sources, reason about their spatial origin, and autonomously navigate toward them under ambient environments.
Yanbaihui Liu, Erica Babusci, Claudia K. Gunsch +1
Compositional diffusion planners aim to solve long-horizon robotic tasks using short training trajectories. Yet, current approaches often rely on the heuristic stitching of local predictions. We show that the resulting stitched update is generally a non-conservative field} that does not mathematically correspond to any valid global trajectory log-density function. We propose Energy-based Compositional Diffuser (ECD), a framework that formulates the global trajectory as the minimizer of the sum of local bridge potentials. This energy-based perspective defines a conservative correction field and contains a boundary reaction term that heuristic stitching omits. To enable efficient inference, we further introduce a Markov-based score approximation that computes the reaction term via a single block-tridiagonal solve, maintaining time complexity linear in the planning horizon. Empirically, ECD achieves state-of-the-art success rates on a range of OGBench stitching tasks, while nearly matching the inference speed of heuristic stitching methods. Code is available at https://github.com/GradientSpaces/ECD.