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Improving Diffusion Planners by Self-Supervised Action Gating with Energies
🤖AI Summary
Researchers propose SAGE (Self-supervised Action Gating with Energies), a new method to improve diffusion planners in offline reinforcement learning by filtering out dynamically inconsistent trajectories. The approach uses a latent consistency signal to re-rank candidate actions at inference time, improving performance across locomotion, navigation, and manipulation tasks without requiring environment rollouts or policy retraining.
Key Takeaways
- →SAGE addresses brittleness in diffusion planners by penalizing dynamically inconsistent plans using latent prediction errors.
- →The method integrates into existing diffusion planning pipelines without requiring environment rollouts or policy retraining.
- →SAGE uses a Joint-Embedding Predictive Architecture (JEPA) encoder trained on offline state sequences for consistency evaluation.
- →Performance improvements were demonstrated across locomotion, navigation, and manipulation benchmarks.
- →The approach combines feasibility scores with value estimates to make better action selections at test time.
#diffusion-planners#reinforcement-learning#offline-rl#jepa#action-selection#machine-learning#robotics#ai-research
Read Original →via arXiv – CS AI
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