Jee-Hwan Ryu

Myung-Hwan Jeon, Jeongyun Kim, Jee-Hwan Ryu et al.

6D object pose estimation aims to infer the relative pose between the object and the camera using a single image or multiple images. Most works have focused on predicting the object pose without associated uncertainty under occlusion and structural ambiguity (symmetricity). However, these works demand prior information about shape attributes, and this condition is hardly satisfied in reality; even asymmetric objects may be symmetric under the viewpoint change. In addition, acquiring and fusing diverse sensor data is challenging when extending them to robotics applications. Tackling these limitations, we present an ambiguity-aware 6D object pose estimation network, PrimA6D++, as a generic uncertainty prediction method. The major challenges in pose estimation, such as occlusion and symmetry, can be handled in a generic manner based on the measured ambiguity of the prediction. Specifically, we devise a network to reconstruct the three rotation axis primitive images of a target object and predict the underlying uncertainty along each primitive axis. Leveraging the estimated uncertainty, we then optimize multi-object poses using visual measurements and camera poses by treating it as an object SLAM problem. The proposed method shows a significant performance improvement in T-LESS and YCB-Video datasets. We further demonstrate real-time scene recognition capability for visually-assisted robot manipulation. Our code and supplementary materials are available at https://github.com/rpmsnu/PrimA6D.

Joong-Ku Lee, Hyeonseok Choi, Young Soo Park et al.

This paper presents a Learning from Teleoperation (LfT) framework that integrates human expertise with robotic precision to enable robots to autonomously perform skills learned from human operators. The proposed framework addresses challenges in nuclear waste handling tasks, which often involve repetitive and meticulous manipulation operations. By capturing operator movements and manipulation forces during teleoperation, the framework utilizes this data to train machine learning models capable of replicating and generalizing human skills. We validate the effectiveness of the LfT framework through its application to a power plug insertion task, selected as a representative scenario that is repetitive yet requires precise trajectory and force control. Experimental results highlight significant improvements in task efficiency, while reducing reliance on continuous operator involvement.

Younghun Cho, Giseop Kim, Sangmin Lee et al.

Using publicly accessible maps, we propose a novel vehicle localization method that can be applied without using prior light detection and ranging (LiDAR) maps. Our method generates OSM descriptors by calculating the distances to buildings from a location in OpenStreetMap at a regular angle, and LiDAR descriptors by calculating the shortest distances to building points from the current location at a regular angle. Comparing the OSM descriptors and LiDAR descriptors yields a highly accurate vehicle localization result. Compared to methods that use prior LiDAR maps, our method presents two main advantages: (1) vehicle localization is not limited to only places with previously acquired LiDAR maps, and (2) our method is comparable to LiDAR map-based methods, and especially outperforms the other methods with respect to the top one candidate at KITTI dataset sequence 00.

Sang-Goo Jeong, Margaret M. Coad, Laura H. Blumenschein et al.

Pneumatically operated soft growing robots that extend via tip eversion are well-suited for navigation in confined spaces. Adding the ability to interact with the environment using sensors and tools attached to the robot tip would greatly enhance the usefulness of these robots for exploration in the field. However, because the material at the tip of the robot body continually changes as the robot grows and retracts, it is challenging to keep sensors and tools attached to the robot tip during actuation and environment interaction. In this paper, we analyze previous designs for mounting to the tip of soft growing robots, and we present a novel device that successfully remains attached to the robot tip while providing a mounting point for sensors and tools. Our tip mount incorporates and builds on our previous work on a device to retract the robot without undesired buckling of its body. Using our tip mount, we demonstrate two new soft growing robot capabilities: (1) pulling on the environment while retracting, and (2) retrieving and delivering objects. Finally, we discuss the limitations of our design and opportunities for improvement in future soft growing robot tip mounts.

Margaret M. Coad, Laura H. Blumenschein, Sadie Cutler et al.

A new class of continuum robots has recently been explored, characterized by tip extension, significant length change, and directional control. Here, we call this class of robots "vine robots," due to their similar behavior to plants with the growth habit of trailing. Due to their growth-based movement, vine robots are well suited for navigation and exploration in cluttered environments, but until now, they have not been deployed outside the lab. Portability of these robots and steerability at length scales relevant for navigation are key to field applications. In addition, intuitive human-in-the-loop teleoperation enables movement in unknown and dynamic environments. We present a vine robot system that is teleoperated using a custom designed flexible joystick and camera system, long enough for use in navigation tasks, and portable for use in the field. We report on deployment of this system in two scenarios: a soft robot navigation competition and exploration of an archaeological site. The competition course required movement over uneven terrain, past unstable obstacles, and through a small aperture. The archaeological site required movement over rocks and through horizontal and vertical turns. The robot tip successfully moved past the obstacles and through the tunnels, demonstrating the capability of vine robots to achieve navigation and exploration tasks in the field.