Autonomous navigation and exploration in unmapped environments remains a significant challenge in robotics due to the difficulty robots face in making commonsense inference of unobserved geometries. Recent advancements have demonstrated that generative modeling techniques, particularly diffusion models, can enable systems to infer these geometries from partial observation. In this work, we present implementation details and results for real-time, online occupancy prediction using a modified diffusion model. By removing attention-based visual conditioning and visual feature extraction components, we achieve a 73% reduction in runtime with minimal accuracy reduction. These modifications enable occupancy prediction across the entire map, rather than limiting it to the area around the robot where sensor data can be collected. We introduce a probabilistic update method for merging predicted occupancy data into running occupancy maps, resulting in a 71% improvement in predicting occupancy at map frontiers compared to previous methods. Finally, our code and a ROS node for on-robot operation can be found on our website: https://arpg.github.io/scenesense/.

When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. This planning paradigm can lead to unintuitive exploration or replanning latency when entering areas that were previous obstructed from view. To address this we present SceneSense, a real-time 3D diffusion model for synthesizing 3D occupancy information from partial observations that effectively predicts these occluded or out of view geometries for use in future planning and control frameworks. SceneSense uses a running occupancy map and a single RGB-D camera to generate predicted geometry around the platform at runtime, even when the geometry is occluded or out of view. Our architecture ensures that SceneSense never overwrites observed free or occupied space. By preserving the integrity of the observed map, SceneSense mitigates the risk of corrupting the observed space with generative predictions. While SceneSense is shown to operate well using a single RGB-D camera, the framework is flexible enough to extend to additional modalities. Unlike existing models that necessitate multiple views and offline scene synthesis, or are focused on filling gaps in observed data, our findings demonstrate that SceneSense is an effective approach to estimating unobserved local occupancy information at runtime. Local occupancy predictions from SceneSense are shown to better represent the ground truth occupancy distribution during the test exploration trajectories than the running occupancy map. The source code can be found on our website: https://arpg.github.io/scenesense/

Imitation learning (IL) frameworks in robotics typically assume that a domain expert's demonstration always contains a correct way of doing the task. Despite its theoretical convenience, this assumption has limited practical values for an IL-powered robot in real world. There are many reasons for an expert in the real world to provide demonstrations that may contain incorrect or potentially unsafe way of doing a task. In order for IL-powered robots to work in the real world, IL frameworks need to detect such adversarial demonstrations and not learn from them. This paper proposes an IL framework that can autonomously detect and remove adversarial demonstrations, if they exist in the demonstration set, as it directly learns a task policy from the expert. The proposed framework that we term Robust Maximum Entropy behavior cloning (R-MaxEnt) learns a stochastic model that maps states to actions. In doing so, R-MaxEnt solves a minmax problem that leverages the entropy of the model to assign weights to different demonstrations while assigning poor weights to adversarial samples. Our empirical results show that R-MaxEnt outperforms the existing IL approaches in both real and simulated robotics tasks.