6 citations · 7 across the 11 of their papers we have counts for
16 papers
LabOPBench: A Multimodal Benchmark for Evidence-Grounded Laboratory Decision Making
Jingyuan Li, Yujie Liu, Yijie Dai +18
Laboratory experiments involve more than following predefined protocols; they require adaptive decision-making as the experimental state changes. Existing scientific benchmarks rar…
LabCompiler: A Framework for Verifiable Human-Machine Laboratory Execution from Imperfect Protocols
Jingyuan Li, Dongzhan Zhou, Minting Pan +10
Closing the loop of AI-driven science requires reliably translating natural-language protocols into executable laboratory workflows despite protocol defects and heterogeneous hardw…
RoboBridge: A Self-Evolving Embodied Agent Framework for Sim-to-Real Transfer
Chenxi Li, Zhangrui Zhao, Rui Li +8
A key challenge in bringing embodied intelligence into the real world is transferring capabilities from simulation to reality and enabling agents to continually adapt after deploym…
RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation
Chenxi Li, Haiyuan Wan, Rui Li +10
Wet-lab experimentation serves as the gold standard for hypothesis verification in scientific discovery; yet it is inherently labor-intensive, costly, and safety-critical. Embodied…
VPTwin: Real-Sim-Real Video Prediction for Robotic Manipulation Planning
Zhenghao Xiao, Minting Pan, Nantian He +2
While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding errors and physically implausi…
ProtoAct: Turning Wet-Lab Protocols into Embodied Robotic Actions
Zhe Liu, Jiaming Gu, Zhaohui Du +7
Biological wet-lab protocols are written for trained researchers and often leave routine operations, state-dependent conditions, and contextual parameters implicit, making them dif…
BioVLN: A Simulation Platform for Visual Language Navigation in Biomedical Laboratories
Zhe Liu, Quan Lu, Zhaohui Du +7
Biomedical laboratory robots must navigate to instruments before performing experimental procedures. Existing embodied navigation platforms are designed for household environments…
Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
Zhe Liu, Huanbo Jin, Zhaohui Du +10
Wet-lab robots can improve the reproducibility, throughput, and safety of biomedical experiments, but scaling their learning requires customizable simulators for safe and reproduci…
LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
Baochang Ren, Xinjie Liu, Xi Chen +15
Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read lit…
LabBuilder: Protocol-Grounded 3D Layout Generation for Interactable and Safe Laboratory
Jianbao Cao, Zhangrui Zhao, Bohan Feng +15
Automated laboratories hold the promise of accelerating scientific discovery, yet their deployment is bottlenecked by the difficulty of designing safe and executable environments.…
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization
Haochen Yuan, Minting Pan, Yunbo Wang +3
Reinforcement learning (RL) has shown significant promise for sequential portfolio optimization tasks, such as stock trading, where the objective is to maximize cumulative returns…
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach
Minting Pan, Yitao Zheng, Jiajian Li +2
Offline reinforcement learning (RL) enables policy optimization using static datasets, avoiding the risks and costs of extensive real-world exploration. However, it struggles with…
Model-Based Reinforcement Learning with Multi-Task Offline Pretraining
Minting Pan, Yitao Zheng, Yunbo Wang +1
Pretraining reinforcement learning (RL) models on offline datasets is a promising way to improve their training efficiency in online tasks, but challenging due to the inherent mism…
Model-Based Reinforcement Learning with Isolated Imaginations
Minting Pan, Xiangming Zhu, Yitao Zheng +2
World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are indep…
Continual Visual Reinforcement Learning with A Life-Long World Model
Minting Pan, Wendong Zhang, Geng Chen +4
Learning physical dynamics in a series of non-stationary environments is a challenging but essential task for model-based reinforcement learning (MBRL) with visual inputs. It requi…
Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models
Minting Pan, Xiangming Zhu, Yunbo Wang +1
World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios such as autonomous driving, there commonly exists noncontrollabl…