Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning

arXiv:2610.10857 · cs.AI, cs.RO · Submitted 2026-10-07 · Read on arXiv

cs.AI, cs.RO

Submitted: 2026-10-07

Updated: 2026-10-07

Project page: https://keyframe-mnemonics.github.io

Terminology

• Benchmark — 54× in this paper · explained in 2 episodes
• Keyframe Mnemonics — 33× in this paper
• Transformer — 26× in this paper · explained in 2 episodes
• Regret — 22× in this paper · explained in 3 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Core — 7× in this paper · explained in 2 episodes
• Reward Function — 7× in this paper · explained in 2 episodes
• Decision Transformer — 6× in this paper
• Adam — 5× in this paper · explained in 2 episodes
• Benchmarking — 5× in this paper · explained in 3 episodes
• Objective Function — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Q-learning — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Institutional Review Board — 2× in this paper
• Stochastic Gradient Descent — 2× in this paper
• Stochastic Gradient Descent (SGD) — 2× in this paper · explained in 5 episodes
• Action Chunking — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Back-Propagation Through Time — 1× in this paper
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Partially observable Markov decision process (POMDP) — 1× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 1× in this paper
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 5 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
• Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning
• BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
• Unbiasing Truncated Backpropagation Through Time
• ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems
• SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
• HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy
• ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
• MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
• MemoAct: Atkinson-Shiffrin-Inspired Hierarchical Memory-Augmented Policy for Robotic Manipulation
• VPWEM: Non-Markovian Visuomotor Policy with Working and Episodic Memory
• Deep learning generalizes because the parameter-function map is biased towards simple functions
• Learning Memory Mechanisms for Decision Making through Demonstrations
• Understanding deep learning requires rethinking generalization
• Proximal Policy Optimization Algorithms
• How Crucial is Transformer in Decision Transformer?
• Recurrent Action Transformer with Memory
• Learning Long-Context Diffusion Policies via Past-Token Prediction
• History-Aware Visuomotor Policy Learning via Point Tracking
• mindmap: Spatial Memory in Deep Feature Maps for 3D Action Policies

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