Robotic Perception

While current 6D pose estimation benchmarks have reached near-saturation on household objects, they often fail to capture the stochastic and optical complexities of industrial environments. We introduce XYZ-IBD, a high-precision benchmark for object detection and 6D pose estimation specifically designed for industrial bin-picking. XYZ-IBD addresses the domain gap by providing 75 multi-view real-world scenes containing approximately 273k annotated instances of metallic, symmetrical, and specular objects. Unlike existing datasets, our benchmark features high-density stochastic stacking and multi-instance ambiguity, reflecting authentic robotic manipulation challenges. We employ a rigorous multi-stage and semi-automatic annotation pipeline, ensuring sub-millimeter annotation accuracy. The annotations are validated through our designed error quantification scheme, securing the reliability of the annotation quality. In addition to real-world evaluation data, we provide a large-scale complementary synthetic training set that is rendered under a realistic bin-picking simulation. Benchmarking state-of-the-art (SOTA) methods for 2D detection and 6D pose estimation reveals a significant performance degradation compared to standard household benchmarks, highlighting the unsolved challenges of industrial vision. XYZ-IBD establishes a new frontier for robust pose estimation in complex, high-occlusion, and reflective scenarios. The dataset and benchmark are publicly available at https://xyz-ibd.github.io.

The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. Existing off-road datasets often lack sensor diversity and exclude vital modalities like thermal and radar that are critical for operation in degraded conditions (e.g., low visibility or adverse weather). To address these gaps, we introduce a large-scale multimodal off-road dataset with six complementary sensor modalities, along with semantic annotations and GPS traces, to support tasks such as semantic segmentation, object detection, and SLAM. The diverse environmental conditions represented in the dataset present significant real-world challenges, which provide opportunities to develop more robust solutions to support the continued advancement of field robotics, autonomous exploration, and perception systems in natural environments. The dataset can be downloaded at: https://www.unmannedlab.org/the-great-outdoors-dataset/

We present iTeach, a deployable system that lets any co-located human fix a robot's perception failures on the spot without expertise, a workstation, or offline retraining. The operator wears a mixed reality (MR) headset, sees the robot's segmentation predictions overlaid on the real scene, and corrects failures hands-free: rearranging objects (HumanPlay), annotating via gaze and voice, and triggering SAM2 backward mask propagation. Each ~20 s interaction yields 150-300 densely labeled training frames; the system fine-tunes the perception model onboard, keeps the better model, and redeploys, all without leaving the deployment site. The full loop requires only an RGB-D camera, onboard GPU, and an MR headset: any mobile robot, any environment. Starting from 26.1 on cluttered real-world scenes, 45 teaching interactions (13K frames) raise segmentation to 80.7 with no catastrophic forgetting; on three standard benchmarks the model never trained on, performance improves as well. Downstream pick-and-place on SceneReplica reaches 72/100, surpassing a model-based pipeline requiring CAD models. A 12-participant user study confirms non-experts match experts on annotation accuracy (~95% box IoU), speed, and task load (NASA-TLX 21/100). The framework is architecture-agnostic: any fine-tunable perception model can serve as backbone.