5 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…
Continual Quadruped Robots Coordination via Semantic Skill Discovery
Daoqing Wang, Yuchen Xiao, Weixuan Huang +5
Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks. Exis…
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…
FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
Yucen Wang, Rui Yu, Shenghua Wan +2
Foundation Models (FMs) and World Models (WMs) offer complementary strengths in task generalization at different levels. In this work, we propose FOUNDER, a framework that integrat…
Reward Models in Deep Reinforcement Learning: A Survey
Rui Yu, Shenghua Wan, Yucen Wang +4
In reinforcement learning (RL), agents continually interact with the environment and use the feedback to refine their behavior. To guide policy optimization, reward models are intr…