1/ 当今机器人还解决不了日常生活中真正重要的长周期复杂任务,这类任务需要规划、物体识别、物体操作和故障恢复等综合能力。
正是因此,斯坦福的 BEHAVIOR 挑战赛迎来了第二届!去年冠军方案完整任务成功率仅有 12.4%。今年参赛任务更多、评估更优、使用也更便捷。🚨
⏰ 提交截止:2026年10月16日
📣 获奖公布:2026年11月4日
🏆 奖金池:11,000 美元
2/N 真实场景的机器人评估虽然关键但很难规模化:实验难以控制、复现和横向对比。而仿真环境恰好能提供可扩展、可控且可复现的测试平台。
BEHAVIOR-1K 是个开源的仿真测试基准,涵盖1000种日常家务活动,需要长期推理、导航和双臂协同操作,让我们能可规模地衡量机器人AI模型的泛化能力。
arxiv.org/abs/2403.09227
💯2026年BEHAVIOR挑战赛有啥新变化?
1. 任务翻倍,难度升级。
我们把基准测试从50个扩到了100个长周期家庭任务。每个活动平均耗时6分钟,需要导航、规划、记忆和双手协调。目前还没有其他机器人benchmark能达到这个难度级别。
🔍 2. 更大数据集,更好baseline
• 20,000个人体遥操作demo,总计1950小时(比2025年大了2倍)
• RGBD观测数据加上机器人自身感知
• 技能/子任务标注
• 强劲baseline支持:pi0.5, GR00T N1.7
5/N 🧪 评测与提交
为了更好地反映真实部署场景,今年的 BEHAVIOR Challenge 只有一个官方赛道,仅使用机器人本机感知:
• RGB
• 深度
• 本体感知
提交说明和评测细节可在此处查看:
behavior.stanford.edu/challe…
6/N 来,我们一起问几个问题:
❓现有模型能解决完整的人类家务任务吗?
🔀智能体该如何融合控制、记忆和规划?
📉当今模型在哪些方面无法泛化?
📈具身AI到底是靠什么让能力放大的?
7/N 💬 加入 BEHAVIOR 的 Discord 服务器来提问和讨论吧:
discord.gg/bccR5vGFEx
另外我们每周一太平洋时间下午5-6点会在 Zoom 上开放答疑时间。链接见网站。
无论你是机器人领域的老手,还是刚入门的新人,我们都在这里为你提供支持。
8/N 为学生和合作者们的出色工作感到骄傲,由 @drfeifei 领导:@wensi_ai @stefyfren @cgokmenAI @yalcintur36 @minyeongkim_ @BrndaHere2Chl @AndiXu1111 @RavenHuang4 @RuohanZhang76 @jiajunwu_cs
9/N 感谢以下人员的大力支持 @EvansXuHan @jin_lynn808 @Hang_Yin_ @ChengshuEricLi @josiah_is_wong @sanjana__z @YunfanJiang @wenlong_huang @RobobertoMM @YunzhuLiYZ @ManlingLi_ @Weiyu_Liu_ @silviocinguetta @hyogweon Prof. Karen Liu
10/N 感谢 @SimovationInc 为 BEHAVIOR 数据集在模拟中提供高质量的 JoyLo 遥操作数据。BEHAVIOR 基于 @nvidia Omniverse 构建。感谢 @nvidia 的持续支持。
11/N 感谢各位赞助方和支持者的慷慨支持。@SimovationInc @IMDAsg @StanfordHAI @SchmidtFutures Calder Inc.
查看英文原文
1/N Long horizon, complex tasks that truly matter in everyday life are not solved problems by today’s robotics, requiring planning, object detection, object manipulation, and failure recovery.
That's why Stanford's BEHAVIOR Challenge is back for year 2! Last year, the winning solution reached only 12.4% full task success. This year, the BEHAVIOR challenge has more tasks, better evaluation, and is easier to use. 🚨
⏰ Submission deadline: 10/16/2026
📣 Winners announced: 11/04/2026
🏆 Prize pool: $11,000
2/N Real-world robot evaluation is essential but hard to scale: experiments are difficult to control, reproduce, and compare. Simulation is a powerful testbed for scalable, controlled, reproducible evaluation.
BEHAVIOR-1K is an open-source simulation benchmark of 1,000 everyday household activities requiring long-horizon reasoning, navigation, and bimanual manipulation, giving us a scalable way to measure how well robot AI models generalize.
arxiv.org/abs/2403.09227
💯What’s new in the 2026 BEHAVIOR Challenge?
1. Double the tasks, double the challenge.
We doubled the benchmark from 50 to 100 long-horizon household tasks. These activities average 6 minutes each, requiring navigation, planning, memory, and bimanual coordination. No other robotics benchmark comes close in terms of difficulty.
🔍 2. Larger dataset, better baselines
• 20,000 human teleoperation demos, 1950 hours in total (2 times larger than 2025)
• RGBD observations and Robot proprioception
• Skill/subtask annotations
• Strong baseline support: pi0.5, GR00T N1.7
5/N 🧪 Evaluation & Submission
To better reflect real-world deployment, the BEHAVIOR Challenge has one official track this year using only robot onboard observations:
• RGB
• Depth
• Proprioception
Submission instructions and evaluation details are available here:
behavior.stanford.edu/challe…
6/N Together, let’s ask:
❓Can current models solve complete human-centered household tasks?
🔀 How should agents combine control, memory, and planning?
📉 Where do today’s models fail to generalize?
📈 What actually scales in embodied AI?
7/N 💬 Join the BEHAVIOR Discord server to ask questions and discuss:
discord.gg/bccR5vGFEx
We will also hold office hours every Monday, 5–6pm PST over Zoom. See the website for the link.
Whether you’re a robotics veteran or just entering the field, we’re here to support you.
8/N Proud of the amazing work from our students and collaborators, led by
@drfeifei
:
@wensi_ai
@stefyfren
@cgokmenAI
@yalcintur36
@minyeongkim_
@BrndaHere2Chl
@AndiXu1111
@RavenHuang4
@RuohanZhang76
@jiajunwu_cs
9/N And with the strong support from
@EvansXuHan
@jin_lynn808
@Hang_Yin_
@ChengshuEricLi
@josiah_is_wong
@sanjana__z
@YunfanJiang
@wenlong_huang
@RobobertoMM
@YunzhuLiYZ
@ManlingLi_
@Weiyu_Liu_
@silviocinguetta
@hyogweon
Prof. Karen Liu
10/N We thank
@SimovationInc
for providing high-quality JoyLo teleoperation data in simulation for the BEHAVIOR dataset.
BEHAVIOR is built upon
@nvidia
Omniverse. We thank
@nvidia
for their continuous support.
11/N We thank our sponsors and supporters for their generous support.
@SimovationInc
@IMDAsg
@StanfordHAI
@SchmidtFutures
Calder Inc.