DRAP: Dynamics-Risk Adaptive Plasticity for Continual Learning in Physical AI
DRAP is a replay-free continual learning method for physical AI that estimates the historical importance of adaptable model tensors using action gradients weighted by local trajectory-amplification risk.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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DRAP is a replay-free continual learning method for physical AI that estimates the historical importance of adaptable model tensors using action gradients weighted by local trajectory-amplification risk. A ridge-regularized local linear model fitted to expert transitions yields a Lyapunov-inspired estimate of one-step perturbation amplification. Risk-weighted squared gradients are accumulated into a tensor-level importance memory, which scales each tensor's effective AdamW learning rate in later tasks. It is instantiated on a KappaTune-selected subnetwork of OpenVLA and evaluated on LIBERO Spatial, Object, and Goal suites against AdamW fine-tuning and EWC.
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DRAP is replay-free, storing no old data, and uses tensor-level importance memory to scale per-tensor learning rates.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseTrajectory-amplification risk is estimated from the largest singular value of a ridge-regularized local linear model fitted to expert transitions.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseOn OpenVLA across LIBERO Spatial, Object, and Goal suites, the paper reports improved retention versus AdamW fine-tuning and EWC.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
Sequential adaptation of robot policies often causes forgetting of earlier behaviors, and storing old data is costly. A replay-free method that protects components critical to past behavior at sensitive trajectory points could make continual adaptation of large VLA policies more practical.
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