Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors
Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors
The paper presents Vision-Predictor-Action (VPA), a method for efficient embodied AI that uses a neuro-symbolic JEPA predictor to generate robot actions. It replaces autoregressive decoders with a lightweight latent control loop, enabling faster inference and improved closed-loop control. Evaluated on LIBERO-10, the model achieves 40.0% success in 200 episodes with a single GPU and under 20M parameters.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
evaluation episodes
200
training time
<3 hours
inference time per decision step
10–14
checkpoint size
<75
- code: Available Artifact link Open source S5
- code: Available Artifact link Open source S5
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
mera reference configuration takes 10-14 ms on an Apple M4; and trained only on LIBERO-10 demonstrations in under three hours on a single GPU, a two-camera policy succeeded in 40.0% of 200 evaluation episodes. These preliminary results from a single training run suggest the approach is workable. Code and weights are available at https://github.com/ylee-sling/Vision-Predictor-Action and https://huggingface.co/ylee-sli
Open source S5
horizon. We analyze inference complexity, regularization against representation collapse, and conditions under which latent sub-goal thresholding limits error accumulation across task phases. As an initial proof of concept, we evaluated VPA on the LIBERO-10 suite. The trainable model has under 20M parameters (checkpoint under 75 MB, excluding a frozen 63.4M-parameter CLIP text encoder); a decision step of a single-ca
Open source S4
ng/vpa-libero10, and archived at https://doi.org/10.5281/zenodo.22960920.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org