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  1. Immersive robotic avatars have the potential to aid and replace humans in a variety of applications such as telemedicine and search-and-rescue operations, reducing the need for travel and the risk to people working in dangerous environments. Many challenges, such as kinematic differences between people and robots, reduced perceptual feedback, and communication latency, currently limit howwell robot avatars can achieve full immersion. This paper presents AVATRINA, a teleoperated robot designed to address some of these concerns and maximize the operator’s capabilities while using a commodity light-weight human–machine interface. Team AVATRINA took 4th place at the recent $10 million ANA Avatar XPRIZE competition, which required contestants to design avatar systems that could be controlled by novice operators to complete various manipulation, navigation, and social interaction tasks. This paper details the components of AVATRINA and the design process that contributed to our success at the competition. We highlight a novel study on one of these components, namely the effects of baseline-interpupillary distance matching and head mobility for immersive stereo vision and hand-eye coordination. 
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    Free, publicly-accessible full text available January 14, 2025
  2. Disinfection robots have applications in promoting public health and reducing hospital acquired infections and have drawn considerable interest due to the COVID-19 pandemic. To disinfect a room quickly, motion planning can be used to plan robot disinfection trajectories on a reconstructed 3D map of the room’s surfaces. However, existing approaches discard semantic information of the room and, thus, take a long time to perform thorough disinfection. Human cleaners, on the other hand, disinfect rooms more efficiently by prioritizing the cleaning of high-touch surfaces. To address this gap, we present a novel GPU-based volumetric semantic TSDF (Truncated Signed Distance Function) integration system for semantic 3D reconstruction. Our system produces 3D reconstructions that distinguish high-touch surfaces from non-high-touch surfaces at approximately 50 frames per second on a consumer-grade GPU, which is approximately 5 times faster than existing CPU-based TSDF semantic reconstruction methods. In addition, we extend a UV disinfection motion planning algorithm to incorporate semantic awareness for optimizing coverage of disinfection trajectories. Experiments show that our semantic-aware planning outperforms geometry-only planning by disinfecting up to 20% more high-touch surfaces under the same time budget. Further, the real-time nature of our semantic reconstruction pipeline enables future work on simultaneous disinfection and mapping. Code is available at: RA-SLAM 
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  3. null (Ed.)