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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 →
37 results for “LIBERO” · page 1 of 2
Research · Robotics & embodied AI
Video Prediction Policy 2: Predict Better, Act Better
VPP2 combines manipulation-focused video pretraining with action learning while preserving the video model’s ability to generalize.
LIBERO-ID: 98.8% overall success rate (as reported)
Robotera, Tsinghua University, HKUST (GZ)
Research · Robotics & embodied AI
Embodiment-Classifier Guidance: Transferring Frozen VLAs Across Robot Dynamics without Demonstrations
Han and Niranjani steer a frozen flow-based VLA with a lightweight embodiment classifier, raising success from 72% to 80% on modified LIBERO controllers.
LIBERO: 80% success rate (as reported)
UC Berkeley
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
PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations
PDRVLA extracts compact physical tokens from VLM features via future prediction objectives, achieving high success rates on four simulation benchmarks without online video generation.
LIBERO: 99.3% success rate (as reported)
Peking University, JD.com
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
REKI: Registering Knowledge in VLAs
REKI combines public-data pretraining with cached register tokens that preserve recent visual context at less than 1 percent inference overhead.
LIBERO: 97.5% success rate (as reported)
Kyutai, Inria, École nationale des ponts et chaussées
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: 97.8% success rate (as reported)
Hong Kong Embodied AI Lab, CUHK, DAMO Academy, Alibaba Group
Research · Efficiency & systems
Serving Agentic Robot Fleets: Workload Characterization, KV-Cache Thrashing, and Admission Control
In sustained replay of robot workloads, limiting active episodes restored shared computing throughput and roughly quadrupled completed programs.
LIBERO-PRO: 1 VLA capacity under 1% starvation SLO (robots) (as reported)
Huawei Inc.
Research · Robotics & embodied AI
SynthDemo-RL: Breaking the Zero-Reward Barrier in VLA Adaptation with LLM-Guided Synthetic Demonstrations
An automated teacher supplied successful manipulation trajectories that enabled subsequent reinforcement learning on tasks where direct PPO received no reward.
LIBERO: 96% success rate (as reported)
Fujitsu Limited, The Institute of Statistical Mathematics
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: 31.05 calls average model calls per episode (as reported)
HKUST(GZ), CUHK, Knowin AI
Research · Robotics & embodied AI
Long-WAM: Scaling the Context of World-Action Models
Long-WAM scales visual history up to 19.2 seconds for action prediction, achieving high success on dynamic tasks.
LIBERO: 99.5% average success rate (as reported)
NVIDIA, MIT, HKU
Research · Code & software
RAPID: Robot Agentic Programming from Demonstrations
RAPID infers task goals, action primitives and a verification environment from a visual demonstration, then iteratively refines executable robot code.
Massachusetts Institute of Technology, National University of Singapore, University of Pennsylvania
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: 93.1% average success rate (as reported)
Drexel University, Virginia Tech, Amazon Store Foundation AI
Research · Robotics & embodied AI
2AM: Grounding Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation
2AM separates task memory from motor control, using language and optional 2D hints to guide a single RGB-only action model.
LIBERO-Mem: 63% relaxed success (as reported)
KU Leuven, Meituan Inc., Flanders Make@KU Leuven
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
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
GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies
GeoAAC uses the geometry of flow matching denoising trajectories to adjust the action chunk size per step, boosting real-world success rates from 53.3% to 74.4%.
real-world manipulation tasks: 74.4% success rate (as reported)
Tongji University, The Hong Kong Polytechnic University, University of Electronic Science and Technology of China