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3 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

   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

3. 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: 84.8% success rate (as reported)

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