SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation

• URL: http://arxiv.org/abs/2608.09771v1
• Date: Mon, 10 Aug 2026 15:58:39 GMT
• Title: SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation
• Abstract summary: We present SLIM, a compact latent interaction policy.<n>It learns representations through self-supervised masked trajectory prediction.<n>SLIM matches or exceeds representative large-scale VLA and world-action-model baselines with fewer parameters.
• Score: 35.54319311306262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language-action policies rely on large multimodal backbones to jointly perform perception, language conditioning, and action generation at every control step. Much of this capacity supports open-domain semantics, whereas continuous robot manipulation primarily requires compact representations of observations, actions, and the transitions induced by actions. Pixel-level world models provide another route, but predicting visual details irrelevant to control can be unnecessarily expensive. We propose SLIM (Self-supervised Latent Interaction Model), a compact 0.5B-parameter latent interaction policy. SLIM learns action-grounded predictive latents that capture both action-conditioned future transitions and the actions that explain observed changes. SLIM learns these representations through self-supervised masked trajectory prediction, combining action reconstruction with future-latent prediction. A compact Mixture-of-Transformers (MoT) backbone models interactions between observation latents and action tokens. The resulting policy is trained with flow matching for language-conditioned action generation. Across simulation benchmarks and real-world evaluation, SLIM matches or exceeds representative large-scale VLA and world-action-model baselines with fewer parameters, no additional embodied pretraining, lower inference latency, and substantially lower GPU memory usage.

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