Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning
Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning
The paper proposes Keyframe Mnemonics, a self-supervised method that discovers critical observations (mnemon,ics) to enable behavior cloning over long horizons. The method achieves 100% success rates in synthetic domains and improves performance in real-world robot manipulation tasks by 13.9% across 23 tasks.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
horizons
20
tasks
23
- code: Available Artifact link Open source S5
- paper: not_reported
- dataset: not_reported
- weights: not_reported
Source excerpts and review record
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onditions 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 gene
Open source S4
ralize 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 https://keyframe-mnemonics.github.io.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org