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8 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: 83.4% success rate (as reported)

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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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