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PDRVLA is introduced, whose lightweight Physical Dynamics Module extracts physical tokens from vision--language features to condition action generation without online video generation, andlations show improved future prediction with world-model supervision and evaluate how subtask conditioning, future object-pose super...
InternVLA-A1.5 is presented, which builds the policy on a native VLM backbone that keeps training on VQA and subtask prediction, and attaches a lightweight unified expert for continuous action generation, and achieves the best overall results on all six simulation benchmarks.
This work presents REAL, an agentic framework for open-world mobile manipulation, which establishes sim-to-real-consistent environment APIs without oracle perception and integrates a simulated user to enable human-in-the-loop interaction.
GAUGE, a real-world-grounded diagnostic benchmark for jointly evaluating how numerical simulators and generative video world models reproduce or deviate from real-world physics, is introduced and no uniformly faithful physics engine is revealed.
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