5 papers
Transferring the Intelligence of VLMs to Robotic Control
Meng-Hao Guo, Zhe-Han Mo, Jia-Jun Wang +6
Humans can seamlessly adapt to both physical and digital worlds, suggesting that while a digital-to-real gap exists in embodiment, environment and task, human intelligence itself m…
Efficient Coordination with the System-Level Shared State: An Embodied-AI Native Modular Framework
Yixuan Deng, Tongrun Wu, Donghao Wu +5
As Embodied AI systems move from research prototypes to real world deployments, they tend to evolve rapidly while remaining reliable under workload changes and partial failures. In…
Value-Guided Iterative Refinement and the DIQ-H Benchmark for Evaluating VLM Robustness
Hanwen Wan, Zexin Lin, Yixuan Deng +1
Vision-Language Models (VLMs) are essential for embodied AI and safety-critical applications, such as robotics and autonomous systems. However, existing benchmarks primarily focus…
Multi-Reward GRPO Fine-Tuning for De-biasing Large Language Models: A Study Based on Chinese-Context Discrimination Data
Deng Yixuan, Ji Xiaoqiang
Large Language Models (LLMs) often exhibit implicit biases and discriminatory tendencies that reflect underlying social stereotypes. While recent alignment techniques such as RLHF…
GenTe: Generative Real-world Terrains for General Legged Robot Locomotion Control
Hanwen Wan, Mengkang Li, Donghao Wu +4
Developing bipedal robots capable of traversing diverse real-world terrains presents a fundamental robotics challenge, as existing methods using predefined height maps and static e…