HELM raises long-horizon VLA success from 58.4% to 81.5% on LIBERO-LONG by combining episodic memory retrieval, learned failure prediction, and replanning, outperforming context extension or adaptation alone.
IEEE International Conference on Robotics and Automation (ICRA) , year =
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A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.
citing papers explorer
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HELM: Harness-Enhanced Long-horizon Memory for Vision-Language-Action Manipulation
HELM raises long-horizon VLA success from 58.4% to 81.5% on LIBERO-LONG by combining episodic memory retrieval, learned failure prediction, and replanning, outperforming context extension or adaptation alone.
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Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.