6 citations · 11 across the 26 of their papers we have counts for
27 papers
StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models
Zhe Liu, Jinghua Hou, Yuxiang Lu +7
Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a single-frame paradigm, limiting…
SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control
Ruihua Han, Rui Gao, Zhe Liu +6
Safe and efficient shape-aware navigation in heterogeneous crowds and robot fleets remains challenging. Traditional approaches often assume homogeneous robots, sparse workspaces, s…
Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation
Runhui Huang, Qihui Zhang, Zhe Liu +3
In this paper, we propose SpectraReward, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning. I…
ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI
Brain Team, Ziyang Gong, Haoming Gu +28
Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, monitor progress, and improve f…
EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
Junwei Luo, Shuai Yuan, Zhenya Yang +3
Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. In this paper, we view this t…
FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
Xirui Li, Zhe Liu, Xiaoqing Ye +4
Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are confined to a fixed action voca…