We present NeuralLabeling, a labeling approach and toolset for annotating 3D scenes using either bounding boxes or meshes and generating segmentation masks, affordance maps, 2D bounding boxes, 3D bounding boxes, 6DOF object poses, depth maps, and object meshes. NeuralLabeling uses Neural Radiance Fields (NeRF) as a renderer, allowing labeling to be performed using 3D spatial tools while incorporating geometric clues such as occlusions, relying only on images captured from multiple viewpoints as input. To demonstrate the applicability of NeuralLabeling to a practical problem in robotics, we added ground truth depth maps to 30000 frames of transparent object RGB and noisy depth maps of glasses placed in a dishwasher captured using an RGBD sensor, yielding the Dishwasher30k dataset. We show that training a simple deep neural network with supervision using the annotated depth maps yields a higher reconstruction performance than training with the previously applied weakly supervised approach. We also show how instance segmentation and depth completion datasets generated using NeuralLabeling can be incorporated into a robot application for grasping transparent objects placed in a dishwasher with an accuracy of 83.3%, compared to 16.3% without depth completion. Supplementary URI: https://florise.github.io/neural_labeling_web/.

We propose a technique for depth completion of transparent objects using augmented data captured directly from real environments with complicated geometry. Using cyclic adversarial learning we train translators to convert between painted versions of the objects and their real transparent counterpart. The translators are trained on unpaired data, hence datasets can be created rapidly and without any manual labeling. Our technique does not make any assumptions about the geometry of the environment, unlike SOTA systems that assume easily observable occlusion and contact edges, such as ClearGrasp. We show how our technique outperforms ClearGrasp in a dishwasher environment, in which occlusion and contact edges are difficult to observe. We also show how the technique can be used to create an object manipulation application with a humanoid robot. Supplementary URI: https://ftorise.github.io/faking_depth_web/.

When humans see a scene, they can roughly imagine the forces applied to objects based on their expe-rience and use them to handle the objects properly. This paper considers transferring this “force-visualization” ability to robots. We hypothesize that a rough force distribution (named “force map”) can be utilized for object manipulation strategies even if accurate force estimation is impossible. Based on this hypothesis, we propose a training method to predict the force map from vision. To investigate this hypothesis, we generated scenes where objects were stacked in bulk through simulation and trained a model to predict the contact force from a single image. We further applied domain randomization to make the trained model function on real images. The experimental results showed that the model trained using only synthetic images could predict approximate patterns representing the contact areas of the objects even for real images. Then, we designed a simple algorithm to plan a lifting direction using the predicted force distribution. We confirmed that using the predicted force distribution contributes to finding natural lifting directions for typical real-world scenes. Furthermore, the evaluation through simulations showed that the disturbance caused to surrounding objects was reduced by 26 % (translation displacement) and by 39 % (angular displacement) for scenes where objects were overlapping.

This paper describes a 3D textured map generation method for autonomous vehicle in urban outdoor environment, where GPS signals can not be reached. Constructed map will be used for short cycle and accurate localization and for obstacle detection using onbody laser scanner. In order to build dense 3D polygon map, planar feature of laser scanner input is extracted. They are associated and transformation matrices in between each scan point were iteratively solved. Aligned points were converted into texture mapped 3D polygons. 400×350[m] area in Univ. of Tokyo were scanned at 59 scan points, and 3D polygon map consists of 14M polygon were obtained. Experimental results of localization and autonomous path following two-wheeled inverted mobile robot PMR are shown.

This paper describes a navigation system in a dynamic environment for a two-wheeled inverted pendulum mobile robot, PMR. Our system is organized by localization, detection and tracking of pedestrians, and trajectory planner. The localization is robust to effects of moving obstacles and pitching movements of the robot, and the trajectory planner creates a path with a certain smoothness considering movements of pedestrians. In addition, the planner introduces strategies to avoid pedestrians to be friendly to pedestrians around the robot. Besides, our system can run on two laptop PCs in real time. Finally, we show experimental results as well.