1k citations · 1.1k across the 8 of their papers we have counts for
8 papers
Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
He Zhang, Lingzhu Xiang, Haitao Lin +23
In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pr…
RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination
Haotian Liang, Mingkang Chen, Yufei Huang +27
Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation…
Hy-Embodied-VLM-1.0: Efficient Physical-World Agents
Ziyi Wang, Xumin Yu, Yongming Rao +19
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situatio…
ProAct: A Benchmark and Multimodal Framework for Structure-Aware Proactive Response
Xiaomeng Zhu, Fengming Zhu, Weijie Zhou +8
While passive agents merely follow instructions, proactive agents align with higher-level objectives, such as assistance and safety by continuously monitoring the environment to de…
EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents
Zihao Zhu, Bingzhe Wu, Zhengyou Zhang +3
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. The emergence of foundation models as the "brain" of EAI age…
Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Lei Han, Qingxu Zhu, Jiapeng Sheng +16
Knowledge from animals and humans inspires robotic innovations. Numerous efforts have been made to achieve agile locomotion in quadrupedal robots through classical controllers or r…
Zeroth-Order Supervised Policy Improvement
Hao Sun, Ziping Xu, Yuhang Song +4
Policy gradient (PG) algorithms have been widely used in reinforcement learning (RL). However, PG algorithms rely on exploiting the value function being learned with the first-orde…
Deeply-Supervised Nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher +2
Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an…