Jialun Li

Hanqing Wang, Jiahe Chen, Wensi Huang et al.

Recent works have been exploring the scaling laws in the field of Embodied AI. Given the prohibitive costs of collecting real-world data, we believe the Simulation-to-Real (Sim2Real) paradigm is a crucial step for scaling the learning of embodied models. This paper introduces project GRUtopia, the first simulated interactive 3D society designed for various robots. It features several advancements: (a) The scene dataset, GRScenes, includes 100k interactive, finely annotated scenes, which can be freely combined into city-scale environments. In contrast to previous works mainly focusing on home, GRScenes covers 89 diverse scene categories, bridging the gap of service-oriented environments where general robots would be initially deployed. (b) GRResidents, a Large Language Model (LLM) driven Non-Player Character (NPC) system that is responsible for social interaction, task generation, and task assignment, thus simulating social scenarios for embodied AI applications. (c) The benchmark, GRBench, supports various robots but focuses on legged robots as primary agents and poses moderately challenging tasks involving Object Loco-Navigation, Social Loco-Navigation, and Loco-Manipulation. We hope that this work can alleviate the scarcity of high-quality data in this field and provide a more comprehensive assessment of Embodied AI research. The project is available at https://github.com/OpenRobotLab/GRUtopia.

Yuansheng Chen, Yue Zhang, Xuan Mo et al.

With the rapid growth of interactive applications in large language model (LLM) online services, maintaining high system throughput while ensuring user-perceived latency has become a key issue in inference scheduling. Existing LLM service systems rely on coarse-grained output constraints, making it difficult to effectively handle resource contention among multiple requests, resulting in low resource utilization efficiency and limited support for fine-grained quality of service (QoS) differentiation. We present SlidingServe, a sliding-window-driven SLO-Aware scheduling system for online LLM inference. SlidingServe designed a lightweight batch latency predictor to estimate the execution time of a batch. Based on this, SlidingServe uses SlidingChunker to combine information from the current iteration and the next iteration to achieve dynamic chunking and improve the overall system throughput while maintaining strict QoS guarantees. SlidingServe introduces Multi-Level Priority Sorter to sort candidate requests in order to balance fairness and efficiency. Additionally, when multiple requests within the same batch are at risk of SLO violating,SlidingServe introduces BatchConstructor, which uses dynamic programming to select the set of requests to execute in the current round, mitigating the SLO violation risk of critical requests.Our evaluation demonstrates that SlidingServe can improve service capacity by up to 30% compared to advanced scheduling systems under various load conditions, and further reduces the rate of SLO violation by 16%-53% under heavy-load inference mode.

Jialun Li, Xiaojia Xie, Hengbo Ma et al.

To generate safe and real-time trajectories for an autonomous vehicle in dynamic environments, path and speed decoupled planning methods are often considered. This paper studies speed planning, which mainly deals with dynamic obstacle avoidance given the planning path. The main challenges lie in the decisions in non-convex space and the trade-off between safety, comfort and efficiency performances. This work uses dynamic programming to search heuristic waypoints on the S-T graph and to construct convex feasible spaces. Further, a piecewise Bezier polynomials optimization approach with trapezoidal corridors is presented, which theoretically guarantees the safety and optimality of the trajectory. The simulations verify the effectiveness of the proposed approach.