论文/arXiv 2610.12435·20 天前

VioLA：从人类数据学习通用人形机器人控制策略

VioLA通过预测身体与手部运动潜变量并由控制器执行，利用人类数据训练，在真机上验证零样本运动和操作。

AI 整理

• 本体 SDK 与控制
• 数据与采集
• 具身模型

原文链接：VioLA: Learning Generalist Humanoid Control Policies from Human Data（arXiv 2610.12435 · 发布日期：2026-10-08）

VioLA: Learning Generalist Humanoid Control Policies from Human Data

VioLA通过预测身体与手部运动潜变量并由控制器执行，利用人类数据训练，在真机上验证零样本运动和操作。

核心要点

• 问题在于人形机器人全身动作空间大且耦合，机器人示教稀缺，难以直接泛化到新指令。
• VioLA不预测关节命令，而预测身体和手部运动潜变量，并由预训练控制器在机器人上执行。
• 运动编码器将人类与机器人运动映射到同一潜空间，使包含140.6百万帧的数据可用于训练。
• 真机零样本运动成功率达100%，操作成功率达88.6%；代码和检查点将发布。

验证范围与局限：验证范围为真实机器人上的运动指令和操作任务，并跨两种VLA与一种world-action模型骨干测试。

英文摘要 (Abstract)

Teaching a humanoid to follow instructions with its whole body runs into two obstacles. Its action space is large and tightly coupled: legs, arms, and fingers must move together while the robot keeps its balance, which makes joint-level actions hard to learn. And humanoid demonstrations are scarce, so current humanoid generalist policies do not follow new instructions out of the box and are fine-tuned on teleoperated demonstrations of each task before deployment. Human demonstrations exist in far larger numbers, but a person's motion is not a robot command. We remove both obstacles by changing what the generalist policy predicts. We introduce VioLA, a generalist humanoid policy that predicts body and hand motion latents instead of joint commands. A pretrained body- and hand-controller execute these latents on the robot. Their corresponding motion encoders map human and robot motion into the same latent spaces. A human recording is therefore labeled in the policy's action space, and the training demonstration pool contains 140.6 million frames, 93.2% of them human. As a result, VioLA follows locomotion instructions on the real robot zero-shot, without task-specific fine-tuning, reaching 100% success where GR00T N1.7 and $Ψ_0$ reach 16.7% and 0%, respectively. It also reaches 88.6% manipulation success without task-specific fine-tuning. The same approach works across two VLA and one world-action model backbones. A generalist policy trained on human demonstrations alone performs locomotion tasks on the real robot zero-shot. Code and checkpoints will be released.

作者：Berat Mert Albaba, Jens Beißwenger, Anna Manasyan, Daniel Marta, Michael J. Black, Wieland Brendel, Andreas Krause, Georg Martius, Martin Riedmiller

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.12435：VioLA: Learning Generalist Humanoid Control Policies from Human Data访问 2026-10-21