October 3, 2025Open Access

Imagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation

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Imagine2Act effectively couples object transformation with action prediction, enhancing robotic manipulation precision.
The system incorporates semantic and geometric constraints to overcome limitations seen in previous approaches.
Experiments reveal that Imagine2Act outperforms state-of-the-art policies, improving performance in relational object rearrangement tasks.
Its object-action consistency strategy allows for accurate action predictions across diverse tasks in both simulation and real-world settings.

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This framework improves robotic manipulation by integrating semantic reasoning and geometric relationships in its learning process.

Heng et al. (2025) studied this question.

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