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.
Multitask learning
4 Pith papers cite this work. Polarity classification is still indexing.
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NetTailor adapts CNN architecture for new tasks by assembling pre-trained universal blocks with task-specific layers, trained via activation mimicry and complexity penalties to match accuracy while reducing size for simpler tasks.
A multi-task self-supervised approach trains a temporal CNN to detect transformations on sensory data, yielding features that match or exceed fully supervised performance in semi-supervised and transfer settings for smartphone-based HAR.
Bayesian optimization enables adaptive network pruning rates in lifelong learning, performing heavier pruning on small/simple tasks and milder on large/complex ones.
citing papers explorer
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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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NetTailor: Tuning the Architecture, Not Just the Weights
NetTailor adapts CNN architecture for new tasks by assembling pre-trained universal blocks with task-specific layers, trained via activation mimicry and complexity penalties to match accuracy while reducing size for simpler tasks.
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Multi-task Self-Supervised Learning for Human Activity Detection
A multi-task self-supervised approach trains a temporal CNN to detect transformations on sensory data, yielding features that match or exceed fully supervised performance in semi-supervised and transfer settings for smartphone-based HAR.
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Adaptive Compression-based Lifelong Learning
Bayesian optimization enables adaptive network pruning rates in lifelong learning, performing heavier pruning on small/simple tasks and milder on large/complex ones.