Wonderful to be back from
#CVPR2026, and excited to share the release of our follow-up work:
VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation
VoLo introduces the idea of a physical orchestrator for open-vocabulary, long-horizon manipulation. Our goal is to move toward robots that can reason, plan, act, monitor, and recover by adaptively using VLA/WAMs, vision models, and action primitives as tools.
We introduce three main contributions:
🤖 VoLoAgent — a physical orchestrator that plans, monitors, and recovers by adaptively using, halting, and redirecting robot actions with tools.
📊 RoboVoLo — a high-fidelity benchmark with 126 open-vocabulary long-horizon manipulation tasks spanning common sense, memory/state tracking, complex references, and world knowledge.
📈 A large-scale empirical study comparing action models, code-as-policy systems, TAMP-style systems, and ablations of the VoLoAgent orchestrator, complemented by real-robot experiments.
This work was done during my internship at
@NVIDIA and would not have been possible without my brilliant collaborators: Hugo Hadfield, Alexander Zook,
@mikacuy,
@luke_ch_song,
@erwincoumans,
@xuningy, Faisal Ladhak,
@qu_1006,
@BirchfieldStan, Jonathan Tremblay, and
@robovalts. Huge thanks to everyone!
🔗 Project:
chicychen.github.io/VoLo/
🔗 Previous work, SpaceTools:
spacetools.github.io/
#Robotics #EmbodiedAI #VisionLanguageModels #VLAModels #RobotLearning #NVIDIA #CVPR2026 #LongHorizonManipulation #AI #ComputerVision
