[Submitted on 7 Oct 2026]
Title:Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning
View a PDF of the paper titled Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning, by Prabin Kumar Rath and 2 other authors
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Abstract:Behavior cloning (BC) in non-Markovian environments is a challenging problem because policies have to reason over contextual information over long horizons. Existing policy architectures rely on recurrent or attention-based mechanisms to capture long-term dependencies. However, recurrent models suffer from hidden-state collapse and gradient instability under backpropagation through time, while attention-based models are fundamentally limited by context length. To address these issues, we propose Keyframe Mnemonics, a novel self-supervised method that $\textit{discovers}$ a set of information-critical observations ($\textit{mnemonics}$) by learning an objective from randomly sampled past observations and using it as a reward for keyframe selection. We then train a BC policy that conditions on the discovered keyframes to model the action distribution. Under certain task-structure assumptions, our formulation provides context retention guarantees over an infinite horizon, while maintaining a small set of decision-relevant keyframes in the policy's working memory. We evaluate our method on synthetic memory domains, where mnemonic-conditioned BC policies achieve $100$% success rates (SR) and generalize to horizons orders of magnitude beyond training without performance degradation. Additionally, we evaluate on memory-intensive robot manipulation benchmark, achieving a $13.9$% average absolute SR improvement over the strongest baseline across $23$ tasks and retaining $80$% SR at $20\times$ longer horizons on a real robot. Code and videos are available at this https URL.
Comments: Accepted at NeurIPS 2026
Subjects:
Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2610.10857 [cs.AI]
(or arXiv:2610.10857v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.10857
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Prabin Kumar Rath [view email] [v1] Wed, 7 Oct 2026 20:01:27 UTC (25,647 KB)
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