aiqbench analysis
This paper introduces PLaCA, a novel framework for Vision-Language-Action models that predicts patch-level future latent changes to enhance robotic manipulation without the computational cost of full image generation. By explicitly incorporating these predicted local environmental shifts into action generation, the method significantly improves task success rates and sequence length on standard benchmarks while requiring minimal parameter updates. This work advances the field by bridging the gap between efficient latent dynamics modeling and precise robot control in dynamic environments.
PLaCA: Learning Patch-Level Future Latent Change for Action Generation
arXiv:2610.27657v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models generate robot actions from visual observations and language instructions, but effective manipulation also requires anticipating how the environment will change. Existing world-action models explore future image prediction and latent dynamics modeling. However, generating complete future images can be computationally expensive, while highly compressed latent representations may obscure local changes. To address these challenges, we propose PLaCA, a world-to-action framework built around future latent change. PLaCA directly predicts spatially structured patch-level future latent change from the current multimodal input, preserving local information about expected environmental changes. We introduce patch-level change-aware supervision to emphasize regions undergoing larger changes and a future-to-action interface that explicitly incorporates predicted future latent change into action generation. Experiments on LIBERO-Long show that PLaCA achieves a 79.0% success rate, improving upon the Action-only baseline by 5.0 percentage points. On CALVIN, PLaCA improves the average sequence length from 2.784 to 3.564. These improvements are achieved while updating only 2.95% of the model parameters during the second training stage, demonstrating the effectiveness of future latent change modeling for downstream robotic manipulation.
Abstract:Vision-Language-Action (VLA) models generate robot actions from visual observations and language instructions, but effective manipulation also requires anticipating how the environment will change. Existing world-action models explore future image prediction and latent dynamics modeling. However, generating complete future images can be computationally expensive, while highly compressed latent representations may obscure local changes. To address these challenges, we propose PLaCA, a world-to-action framework built around future latent change. PLaCA directly predicts spatially structured patch-level future latent change from the current multimodal input, preserving local information about expected environmental changes. We introduce patch-level change-aware supervision to emphasize regions undergoing larger changes and a future-to-action interface that explicitly incorporates predicted future latent change into action generation. Experiments on LIBERO-Long show that PLaCA achieves a 79.0% success rate, improving upon the Action-only baseline by 5.0 percentage points. On CALVIN, PLaCA improves the average sequence length from 2.784 to 3.564. These improvements are achieved while updating only 2.95% of the model parameters during the second training stage, demonstrating the effectiveness of future latent change modeling for downstream robotic manipulation.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2610.27657 [cs.RO]
(or arXiv:2610.27657v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.27657 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 273 words