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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 →
16 results for “LIBERO”
Research · Robotics & embodied AI
Trace-WAM: Learning to Act via 3D Motion Prediction for World Action Models
The method, Trace-WAM, jointly predicts future video, 3D motion, and actions, improving performance on several benchmarks and real-world tasks, especially when data is limited.
LIBERO: 98.7% success rate (as reported)
The Hong Kong Polytechnic University, Alibaba Group, Tsinghua University
Research · Robotics & embodied AI
Leap-VLA: High-Rate Robot Control from a Low-Rate VLM Backbone
Cached semantic context plus a fast visual stream allows robot control at 20-50 Hz with 1-second VLM updates, recovering over 94% success rate.
LIBERO Goal: 97.2% success rate (as reported)
Leapmotor
Research · Robotics & embodied AI
RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions
Zhao and colleagues pretrained a robot policy on motion representations extracted from 30,100 hours of video, then adapted it to specific robots and evaluated manipulation performance.
LIBERO: 98.8% success (as reported)
Hong Kong Embodied AI Lab, CUHK, PUMC
Research · Robotics & embodied AI
RynnWorld-Latent: A Cross-Embodiment World Model Conditioned on Universal Latent Actions
Zhao and colleagues report that a model trained across human, robot and simulation videos generates additional demonstrations that improve robot policy success in the evaluated settings.
LIBERO: 96.1% success rate (as reported)
Hong Kong Embodied AI Lab, CUHK, DAMO Academy, Alibaba Group
Research · Robotics & embodied AI
TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency
TacitVLA matched explicit-reasoning policies across four robotics and driving benchmarks while reducing action latency by up to 3.4 times.
LIBERO: 98.6% Success (as reported)
NVIDIA, New York University, University of California, Berkeley
Research · Robotics & embodied AI
World Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal
A multi-agent harness lets vision-language models preview, revise, and visually correct robot actions before and during execution.
LIBERO-Pro: 75.6% average success (as reported)
HKUST(GZ), CUHK, Knowin AI
Research · Robotics & embodied AI
Wiring Matters: Injection Topology and Initialization of Affordance Heads in Vision-Language-Action Policies
A stop-gradient and an actively initialized residual bridge lift LIBERO success from 93.1% to 96.2%, matching far more complex architectures.
LIBERO: 91.4% long-horizon success rate (as reported)
Drexel University, Virginia Tech, Amazon Store Foundation AI
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: 95.9% success rate (as reported)
Columbia University, Toyota Research Institute
Research · Robotics & embodied AI
Spatially Aware World Action Model via Geometric Latent Diffusion
SA-WAM jointly predicts robot actions, RGB frames, and depth while retaining a pretrained video model’s frozen visual tokenizer.
RoboCasa: 76.6% success rate (as reported)
Inria, École Normale Supérieure, PSL Research University
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
InternW0-$Δ$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
InternW0-Delta combines video, semantic, geometric and action models, trained on more than 20,000 hours of heterogeneous demonstrations.
LIBERO-Plus: 92.8% success rate (as reported)
Shanghai AI Laboratory
Research · Vision
ALPS: Visual State Encoders for World Models
ALPS, a ViT-B/16 pretrained from still images and text, matches the state-probe error of a 3.5x larger model while preserving language alignment and pixel decode ability
LIBERO: 85.5% mean policy success (as reported)
University of California, Santa Cruz, University of California, Santa Barbara, Google
Research · Robotics & embodied AI
VLA-ZO: Fast Zeroth-Order Adaptation for Vision-Language-Action Models
VLA-ZO accelerates memory-efficient adaptation by updating only action components and reusing vision-language conditioning across parameter perturbations.
LIBERO camera-viewpoint shifts: 32.54 x speedup (as reported)
UNIST
Research · Robotics & embodied AI
Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution
Robota-Embed matches a robot’s current task and execution stage to useful recorded continuations, improving retrieval and downstream policy performance.
LIBERO difficult subset: 92.8% success (as reported)
NVIDIA, MIT, UCB
Research · Robotics & embodied AI
BIND: Binding 3D Robot Actions to 2D Image Features
BIND links candidate 3D end-effector positions to projected image features, avoiding the need to learn this spatial correspondence from demonstrations.
LIBERO: 92% success rate (as reported)
University of Southern California, Toyota Research Institute
Research · Robotics & embodied AI
Spatial Grafting: Grounding 3D Features for Flow-Matching Robot Policies
A lightweight module grounded reconstruction features in metric robot coordinates and improved four vision-language and world-action policy hosts without redesigning their perceptual backbones.
LIBERO: 99.6% Success Rate (as reported)
University of Toronto, Huawei Noah’s Ark Lab, 2012 Labs