4 papers
DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning
Shuhang Wang, Ziming Li, Hui Cheng
Reinforcement learning is a natural post-training paradigm for code-oriented large language models because generated programs can be evaluated through parsing, execution, unit test…
RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learning
Shuhang Wang, Ziming Li, Hui Cheng
Mobile manipulation requires generating whole-body action chunks that jointly satisfy goal reaching, collision avoidance, base kinematic constraints, manipulator joint limits, and…
Towards Generalizable Robotic Manipulation in Dynamic Environments
Heng Fang, Shangru Li, Shuhan Wang +3
Vision-Language-Action (VLA) models excel in static manipulation but struggle in dynamic environments with moving targets. This performance gap primarily stems from a scarcity of d…
NaviDiffusor: Cost-Guided Diffusion Model for Visual Navigation
Yiming Zeng, Hao Ren, Shuhang Wang +2
Visual navigation, a fundamental challenge in mobile robotics, demands versatile policies to handle diverse environments. Classical methods leverage geometric solutions to minimize…