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Benchmark
Reported in 31 papers on Zotpaper. The latest, each as the paper states it:
LIBERO-Plus (16)LIBERO-PRO (6)LIBERO-Long (3)All benchmarks →
7 results for “LIBERO”
Research · Generative media
RealtimeWAM: One-Step Asynchronous World Action Models
RealtimeWAM uses teacher-anchored consistency distillation and cross-expert pipelining to accelerate Mixture-of-Transformers based world action models by up to 25x on H100 GPUs with less than 1% accuracy loss
LIBERO: 99% success rate (as reported)
Nanyang Technological University, Beihang University, Sensetime
Research · Robotics & embodied AI
SUAVE: Unified Video-Action Models via Masked Diffusion
SUAVE uses masked diffusion over shared discrete tokens to generate video, robot actions, or both with the same model weights.
LIBERO: 95.4% success rate (as reported)
Columbia University, Toyota Research Institute
Research · Robotics & embodied AI
Native Action-Prior Learning from Videos for World Action Models
NAVA-WAM uses visual-transition prediction to train an action model before adapting it with action-labeled robot demonstrations.
LIBERO: 99% success rate (as reported)
Meta AI, University of Copenhagen, Physical Intelligence
Research · Robotics & embodied AI
TAPESTRY: A Single Backbone for Video Generation and Robot Control
The authors show that representing robot actions as single-patch tokens in a pretrained video diffusion transformer's latent space allows real-time action generation without video synthesis.
LIBERO: 70% average success rate (as reported)
Research · Robotics & embodied AI
W2A: Distilling World Priors into Action Models for Real-Time Robotic Control
A two-stage training method transfers what a video prediction model has learned about how scenes unfold into a robot control model, then throws the video model away before deployment.
LIBERO: 95.9% success rate (as reported)
Harbin Institute of Technology, Shenzhen
Research · Robotics & embodied AI
Data-Free Continual Learning for World Action Model with Rectified Generative Replay
Fang et al. show that rectified generative replay, which truncates rollouts at task completion and filters physically inconsistent ones, reduces forgetting by over 90% versus sequential fine-tuning.
LIBERO (average): 94.6% SR (as reported)
University of Electronic Science and Technology of China, Shanghai AI Lab, Fudan University
Research · Robotics & embodied AI
Rolling-WAM: World Action Models with Rolling Imagination
The method reuses partially denoised video and action predictions across control cycles instead of regenerating the full planning horizon.
LIBERO: 98.1% success rate (as reported)
University of Southern California, Brown University, Fudan University