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10 results for “LIBERO-Plus”

1. Research · Robotics & embodied AI

   Future 3D motion prediction boosts robot learning with fewer demonstrations

   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-Plus: 82.4% success rate (as reported)

   30 Oct 2026The Hong Kong Polytechnic University, Alibaba Group, Tsinghua University

2. Research · Robotics & embodied AI

   Compact robot policy uses lightweight memory to stabilize action generation

   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-Plus: 4.3 pts success rate improvement from pretraining (as reported)

   16 Oct 2026Kyutai, Inria, École nationale des ponts et chaussées

3. Research · Robotics & embodied AI

   Robots can anticipate physical changes without generating videos of the future

   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-Plus: 89.9% success rate (as reported)

   28 Oct 2026Peking University, JD.com

4. Research · Robotics & embodied AI

   Adding Depth Helps Video World Models Control Robots More Reliably

   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)

   3 Sept 2026Inria, École Normale Supérieure, PSL Research University

5. Research · Robotics & embodied AI

   Robots can use human videos before learning their own controls

   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-Plus: 84.7% success (as reported)

   30 Oct 2026Hong Kong Embodied AI Lab, CUHK, PUMC

6. Research · Robotics & embodied AI

   Robots learn tasks better with demonstrations generated by a video model

   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-Plus: 79.4% success rate (as reported)

   30 Oct 2026Hong Kong Embodied AI Lab, CUHK, DAMO Academy, Alibaba Group

7. Research · Robotics & embodied AI

   Reasoning supervision lets action models skip decoding without losing performance

   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-Plus: 72.9% Success (as reported)

   14 Oct 2026NVIDIA, New York University, University of California, Berkeley

8. Research · Robotics & embodied AI

   One Model Predicts Robot Futures and Chooses Actions

   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-Plus: 71.3% success rate (as reported)

   6 Oct 2026Columbia University, Toyota Research Institute

9. Research · Robotics & embodied AI

   Observation-only videos can directly pretrain robot action policies

   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-Plus: 83.5% success rate (as reported)

   5 Oct 2026Meta AI, University of Copenhagen, Physical Intelligence

10. Research · Robotics & embodied AI

    Predictive visual models improve action generation for generalist robot manipulation

    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)

    28 Sept 2026Shanghai AI Laboratory