4 papers
BRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Models
Shaowei Zhang, Jiahan Cao, Xunlan Zhou +2
Model-based Reinforcement Learning (MBRL) has achieved remarkable success in continuous control by leveraging latent world models. However, prevailing approaches typically rely on…
TACO: Temporal Consensus Optimization for Continual Neural Mapping
Xunlan Zhou, Hongrui Zhao, Negar Mehr
Neural implicit mapping has emerged as a powerful paradigm for robotic navigation and scene understanding. However, real-world robotic deployment requires continual adaptation to c…
MARVL: Multi-Stage Guidance for Robotic Manipulation via Vision-Language Models
Xunlan Zhou, Xuanlin Chen, Shaowei Zhang +4
Designing dense reward functions is pivotal for efficient robotic Reinforcement Learning (RL). However, most dense rewards rely on manual engineering, which fundamentally limits th…
UDON: Uncertainty-weighted Distributed Optimization for Multi-Robot Neural Implicit Mapping under Extreme Communication Constraints
Hongrui Zhao, Xunlan Zhou, Boris Ivanovic +1
Multi-robot mapping with neural implicit representations enables the compact reconstruction of complex environments. However, it demands robustness against communication challenges…