Spot-On: A Mixed Reality Interface for Multi-Robot Cooperation

Recent progress in mixed reality (MR) and robotics is enabling increasingly sophisticated forms of human-robot collaboration. Building on these developments, we introduce a novel MR framework that allows multiple quadruped robots to operate in semantically diverse environments via a MR interface. Our system supports collaborative tasks involving drawers, swing doors, and higher-level infrastructure such as light switches. A comprehensive user study verifies both the design and usability of our app, with participants giving a "good" or "very good" rating in almost all cases. Overall, our approach provides an effective and intuitive framework for MR-based multi-robot collaboration in complex, real-world scenarios.

Robust Human Registration with Body Part Segmentation on Noisy Point Clouds

Registering human meshes to 3D point clouds is essential for applications such as augmented reality and human-robot interaction but often yields imprecise results due to noise and background clutter in real-world data. We introduce a hybrid approach that incorporates body-part segmentation into the mesh fitting process, enhancing both human pose estimation and segmentation accuracy. Our method first assigns body part labels to individual points, which then guide a two-step SMPL-X fitting: initial pose and orientation estimation using body part centroids, followed by global refinement of the point cloud alignment. Additionally, we demonstrate that the fitted human mesh can refine body part labels, leading to improved segmentation. Evaluations on the cluttered and noisy real-world datasets InterCap, EgoBody, and BEHAVE show that our approach significantly outperforms prior methods in both pose estimation and segmentation accuracy. Code and results are available on our project website: https://segfit.github.io