RoboTwin

Headlines · 2

1. 2d
   EVEWorld cuts world-model 'laziness' rate 85.7% in robot videos

   Researchers at China Merchants Group's Advanced Technology Research Institute and Nanyang Technological University (NTU) propose EVEWorld, a post-training supervision framework that enforces physical consistency of manipulated objects in world models: Instance-Guided Recovery (IGR) builds object-duplication perturbations and trains the model to restore the correct count, while Temporal Instance Alignment (TIA) matches and transfers features across adjacent frames in selected layers of a video diffusion Transformer. The authors define unjustified object duplication or disappearance as "Model Laziness" and measure it with a Model Laziness Rate (MLR). According to the paper, EVEWorld lowers MLR on DreamGenBench from 11.11% to 1.59%, an 85.7% relative drop, while Qwen-IF and Gemini-IF scores rise 6.35 and 7.28 points; on WorldArena 1.0, persistently anomalous videos fall from 43 to 21 of 157 shared evaluable samples (about 51.1% relative), and transferred to FlowWAM it cuts RoboTwin MLR from 47.89% to 35.21%. The authors note the method's limitation: its local pairwise supervision does not explicitly model long-horizon state dependencies or complex multi-object interactions.

   36氪 人工智能 · 🔥 0
2. 3d
   VLA-Δ supervises physical state transitions in vision-language-action models

   The paper proposes VLA-Δ, a framework that treats the physical change between current and future states as an explicit learning target, rather than having vision-language-action (VLA) models map observations directly to demonstrated actions — an approach the authors say is prone to overfitting observation-action correspondences. To support this, the team built a large-scale pretraining dataset and derived physical state-transition annotations covering task-relevant semantics and visual motion changes. At inference the model still generates actions directly from current observations with no added overhead; the authors report performance competitive with the state of the art on LIBERO and RoboTwin and better than π0.5 in real-robot experiments, with code released.

   Hugging Face · Papers · 🔥 0

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