thank you for the kind offer. the true prize is priceless – the quest for spatial and embodied intelligence.
@drfeifei
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BEHAVIOR AI Robotics System Built on Nvidia Omniverse Platform
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(14/N) We thank @SimovationInc for providing high-quality JoyLo teleoperation data in simulation, reflecting their deep expertise in simulation and data quality. BEHAVIOR is built upon @nvidia
’s Omniverse. We thank @nvidia for their continuous support. -
Long-Horizon Tasks and Embodied AI Scaling Laws
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(10/N) Together, let’s explore:
How close are we to solving long-horizon, complex, human-centric tasks?
How to efficiently combine low-level control and high-level planning?
What are the generalization limits of current models? Are there scaling laws for embodied AI? -
Stanford AI Behavior Challenge: Submit Your Work by November
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(9/N) Evaluation & Submission
Submission instructions and evaluation details are available on our website: https://
behavior.stanford.edu/challenge
Start experimenting today and get ready to compete!
Deadline: Nov. 15th
Winners announced: Dec. 1st
NeurIPS challenge: Dec. 6-7, San Diego, CA -
Baseline Models for Robot Learning Experiments
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(8/N) Provided Baselines
We include a set of baselines to kickstart your experiments:
• Classic behavioral cloning models: Diffusion Policy, WB-VIMA, ACT, BC-RNN • Pre-trained VLA models: OpenVLA, π_0 @physical_int -
Diverse State Transitions and Manipulation Skills in Robotics
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(7/N) 🧩Feature #4: Diverse State Transitions and Manipulation Skills
— Fei-Fei Li (@drfeifei) 2 septembre 2025
• Spatial: next_to, inside, on_top, under, touching
• Particles: covered, uncovered
• Thermal: hot, cooked, on_fire, frozen
• Others: open, closed, on, off, attached, sliced, diced pic.twitter.com/AS8KBroBxZ(7/N) Feature #4: Diverse State Transitions and Manipulation Skills
• Spatial: next_to, inside, on_top, under, touching
• Particles: covered, uncovered
• Thermal: hot, cooked, on_fire, frozen
• Others: open, closed, on, off, attached, sliced, diced -
Long-Horizon Mobile Manipulation in Realistic Home Environments
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(6/N) 🏠Feature #3: Long-Horizon Mobile Manipulation in Realistic Homes
— Fei-Fei Li (@drfeifei) 2 septembre 2025
• Task durations range from 1 to 25 minutes (average 6.6 minutes)
• Performed in household-scale scenes
• Requires memory, planning, and reasoning over a long period of time pic.twitter.com/LD2ObgdsxT(6/N) Feature #3: Long-Horizon Mobile Manipulation in Realistic Homes
• Task durations range from 1 to 25 minutes (average 6.6 minutes)
• Performed in household-scale scenes
• Requires memory, planning, and reasoning over a long period of time -
High-Quality Teleoperated Data for Robot Training Systems
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(5/N) 💯Feature #2: High-Quality Data
— Fei-Fei Li (@drfeifei) 2 septembre 2025
✔️Teleoperated using our JoyLo interface
✔️Near-optimal, clean demos
✔️Consistent manipulation behaviors
✔️Moderate, consistent teleoperation speed
🚫 No sudden accelerations/decelerations
🚫 No failed grasps
🚫 No unintended collisions pic.twitter.com/kLNUdwYAE2(5/N) Feature #2: High-Quality Data
Teleoperated using our JoyLo interface
Near-optimal, clean demos
Consistent manipulation behaviors
Moderate, consistent teleoperation speed No sudden accelerations/decelerations No failed grasps No unintended collisions -
Large-Scale Robot Demonstration Dataset with 50 Tasks
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(4/N) 🔍Feature #1: Large-Scale Demonstration Dataset
— Fei-Fei Li (@drfeifei) 2 septembre 2025
• 50 tasks, 10,000 demos, a total of ~1,200 hours of data
• Subtask and skill (30+) segmentation
• Spatial relation annotation
• Multi-granularity language annotation pic.twitter.com/XZDl3aptuU(4/N) Feature #1: Large-Scale Demonstration Dataset
• 50 tasks, 10,000 demos, a total of ~1,200 hours of data
• Subtask and skill (30+) segmentation
• Spatial relation annotation
• Multi-granularity language annotation -
BEHAVIOR Benchmark Advances Embodied AI and Robotics Research
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(3/N) As a reminder, BEHAVIOR is an open-source benchmark built on top of NVIDIA’s Omniverse, designed to enable and evaluate embodied AI and robotics solutions. It includes 1,000 everyday household tasks grounded in human needs. Paper: https://
arxiv.org/abs/2403.09227
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