Self-Supervised Keyframe Discovery for Horizon-Invariant Beh
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: 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…
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Reported quantity · horizons: 20 other · Basis not reported
“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.”
Exact source · revision 1Source owner not reported
Reported quantity · tasks: 23 other · Basis not reported
“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.”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“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.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSelf-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.
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