LIBERO is a new benchmark for lifelong robot learning that evaluates transfer of declarative, procedural, and mixed knowledge across 130 manipulation tasks with provided demonstration data.
Architecture matters in continual learning
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GrapNet defines a graph-as-architecture neural substrate with node-owned child references and allocation vectors that supports structural edits and shows accuracy gains over MLPs in continual learning on Split Fashion-MNIST and CIFAR-10.
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LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
LIBERO is a new benchmark for lifelong robot learning that evaluates transfer of declarative, procedural, and mixed knowledge across 130 manipulation tasks with provided demonstration data.
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GrapNet: A Programmable Dynamic-Architecture Neural Graph Substrate
GrapNet defines a graph-as-architecture neural substrate with node-owned child references and allocation vectors that supports structural edits and shows accuracy gains over MLPs in continual learning on Split Fashion-MNIST and CIFAR-10.