Project

ROBOVERSE comprises a scalable simulation platform, a large-scale synthetic dataset, and unified benchmarks. The simulation platform supports seamless integration of new tasks and demonstrations through unified protocols, ensuring flexibility and extensibility. The dataset includes over 1,000 diverse tasks and more than 10 million transitions, constructed through large-scale data migration, cross-embodiment transfer, and robust augmentation and randomization.

To enhance daily activities and movement, indoor layouts must be easy to navigate and comfortable. We incorporate motion considerations into the design process. For a specific scene, we utilized motion synthesis and inverse dynamics for evaluation, followed by optimization using CMA-ES. This method significantly improved metrics related to human movement distance and energy consumption for object retrieval. Future work will focus on refining metric calculations and integrating more personalized characteristics into indoor scene design.

We propose SimiSketch, a sketching algorithm designed to tackle the specific problem of estimating similarity between sets. We first present two simpler versions that employ intuitive sketches for similarity estimation. Subsequently, we formally introduce SimiSketch and leverage SALSA to enhance accuracy. To validate our algorithms, we conduct extensive testing on synthetic datasets, real-world network traffic, and text articles. Our experiment shows that compared with the state-of-the-art, SimiSketch can improve the accuracy by up to 42 times, and increase the throughput by up to 360 times. The complete source code is open-sourced and available on GitHub for reference.