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ARXIV:2603.08763 · LIFELONG LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.08763LIFELONG LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
SPREAD is a geometry-preserving framework that uses SVD to align policy representations across tasks, improving knowledge transfer and mitigating catastrophic forgetting in lifelong imitation learning.
Opportunity summary
Pain SPREAD is a geometry-preserving framework that uses SVD to align policy representations across tasks, improving knowledge transfer and mitigating catastrophic forgetting in lifelong imitation learning.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
SPREAD is a geometry-preserving framework that uses SVD to align policy representations across tasks, improving knowledge transfer and mitigating catastrophic forgetting in lifelong imitation learning. This requires preserving the low-dimensional manifolds and geometric structures…
A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the LIBERO, lifelong imitation learning benchmark, show that SPREAD substantially improves knowledge transfer, mitigates catastrophic forgetting, and achieves state-of-the-art performance.
Lifelong Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
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SPREAD is a geometry-preserving framework that uses SVD to align policy representations across tasks, improving knowledge transfer and mitigating catastrophic forgetting in lifelong imitation learning.
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10.48550/arXiv.2603.08763SPREAD is a geometry-preserving framework that uses SVD to align policy representations across tasks, improving knowledge transfer and mitigating catastrophic forgetting in lifelong imitation learning.
Abstract
A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations across sequential learning. Existing distillation methods, which rely on L2-norm feature matching in raw feature space, are sensitive to noise and high-dimensional variability, often failing to preserve intrinsic task manifolds. To address this, we introduce SPREAD, a geometry-preserving framework that employs singular value decomposition (SVD) to align policy representations across tasks within low-rank subspaces. This alignment maintains the underlying geometry of multimodal features, facilitating stable transfer, robustness, and generalization. Additionally, we propose a confidence-guided distillation strategy that applies a Kullback-Leibler divergence loss restricted to the top-M most confident action samples, emphasizing reliable modes and improving optimization stability. Experiments on the LIBERO, lifelong imitation learning benchmark, show that SPREAD substantially improves knowledge transfer, mitigates catastrophic forgetting, and achieves state-of-the-art performance.
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PROBLEM
SPREAD is a geometry-preserving framework that uses SVD to align policy representations across tasks, improving knowledge transfer and mitigating catastrophic forgetting in lifelong imitation learning. This requires preserving the low-dimensional manifolds and geometric structur...
METHOD
A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations across seq...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the LIBERO, lifelong imitation learning benchmark, show that SPREAD substantially improves knowledge transfer, mitigates catastrophic forgetting, and achieves state-of-the-art performance.
WHY NOW
Lifelong Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
SPREAD is a geometry-preserving framework that uses SVD to align policy representations across tasks, improving knowledge transfer and mitigating catastrophic forgetting in lifelong imitation learning. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations across sequential learning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations across sequential learning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the LIBERO, lifelong imitation learning benchmark, show that SPREAD substantially improves knowledge transfer, mitigates catastrophic forgetting, and achieves state-of-the-art performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Lifelong Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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SPREAD is a geometry-preserving framework that uses SVD to align policy representations across tasks, improving knowledge transfer and mitigating catastrophic forgetting in lifelong imitation learning.
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