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HuGe 提出了一族人形通用视觉-语言-动作（VLA）模型，利用超过2000小时的第一人称人类与机器人数据进行预训练，并通过伪本体感知与视觉适配实现全身控制，在 HumanoidArena 仿真基准上达到 SOTA，同时在 Unitree G1 实体机器人上验证了其有效性。
该论文提出 HumanVerse——一个500小时的开放世界第一视角人体全身操作数据集，以及基于此训练的人形基础模型 λ_0，实现了无需目标任务机器人演示的零样本迁移到 Unitree G1，在 SIMPLE 上达到 90.0% 成功率。
Reflex提出一个三阶段框架，让类人机器人通过自我中心RGB-D视觉实现对抛掷物体的全身心反射式接取控制，在仿真中达到85.7%成功率，并在真实Unitree G1机器人上以2.5米距离实现65%的接取成功率。
TANGO is a vision-language-action framework for humanoid robots to navigate cluttered environments using simulated training data, demonstrating state-of-the-art performance and zero-shot real-world deployment.
Google DeepMind released Gemini Robotics 2, an AI model that controls entire humanoid robots with whole-body motions and improved dexterity, along with updates to embodied reasoning and on-device models for multi-robot coordination and safety.
Moby, a humanoid robot, demonstrates powerful whole-body control for handling dynamic payloads, marking significant progress over current humanoids.
Humanoid-GPT is a GPT-style Transformer pre-trained on a billion-scale motion corpus, achieving zero-shot generalization for whole-body motion tracking across unseen motions and tasks.
Google DeepMind demonstrated the Gemini Robotics 2 model, enabling robot Apollo to understand natural language instructions, autonomously coordinate movements, and complete multi-step tasks such as packing sports equipment in cluttered environments through full-body control and embodied reasoning, validating the potential of general-purpose robots.
Google DeepMind's Gemini Robotics 2 expands physical AI to whole-body control, enabling humanoid robots to perform tasks requiring reaching, bending, and balancing in cluttered spaces.