# KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies

arXiv: https://arxiv.org/abs/2606.23589 · HTML: https://mlsanity.com/paper/2606.23589/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning and chain-of-thought in VLA, one of 13 directions in Vision-language-action models.
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Placed in

- Vision-language-action models / [Reasoning and chain-of-thought in VLA](https://mlsanity.com/vla/reasoning-and-chain-of-thought-in-vla/)

## Builds on (most cited first, 19 of 19)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [Vision-Language Foundation Models as Effective Robot Imitators](https://mlsanity.com/paper/2311.01378/) (2023)
- [MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2508.19236/) (2025)
- [MemER: Scaling Up Memory for Robot Control via Experience Retrieval](https://mlsanity.com/paper/2510.20328/) (2025)
- [SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation](https://mlsanity.com/paper/2501.18564/) (2025)
- [RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies](https://mlsanity.com/paper/2603.04639/) (2026)
- [HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy](https://mlsanity.com/paper/2510.00695/) (2025)
- [ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context](https://mlsanity.com/paper/2510.04246/) (2025)
- [ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries](https://mlsanity.com/paper/2603.12942/) (2026)
- [CronusVLA: Towards Efficient and Robust Manipulation via Multi-Frame Vision-Language-Action Modeling](https://mlsanity.com/paper/2506.19816/) (2025)
- [BPP: Long-Context Robot Imitation Learning by Focusing on Key History Frames](https://mlsanity.com/paper/2602.15010/) (2026)
- [Coarse-to-Fine Q-attention with Learned Path Ranking](https://mlsanity.com/paper/2204.01571/) (2022)
- [Non-Markovian Long-Horizon Robot Manipulation via Keyframe Chaining](https://mlsanity.com/paper/2603.01465/) (2026)
- [FrameSkip: Learning from Fewer but More Informative Frames in VLA Training](https://mlsanity.com/paper/2605.13757/) (2026)
- [SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation](https://mlsanity.com/paper/2603.05117/) (2026)
- [TGM-VLA: Task-Guided Mixup for Sampling-Efficient and Robust Robotic Manipulation](https://mlsanity.com/paper/2603.00615/) (2026)

## Built on by (most cited first, 5 of 5)

- [ChainVLA: Chaining Vision-Language-Action Queries through a Unified Execution State for Long-Horizon Manipulation](https://mlsanity.com/paper/2608.02326/) (2026)
- [UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling](https://mlsanity.com/paper/2609.11875/) (2026)
- [Remember What You Did: Action-History Memory with Dual-Expert Denoising for Long-Horizon Vision-Language-Action Policies](https://mlsanity.com/paper/2609.37307/) (2026)
- [What to Attend, What to Keep: Skill-Conditioned Visuotactile Representation with Progress-Guided Event Memory](https://mlsanity.com/paper/2609.38494/) (2026)
- [Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies](https://mlsanity.com/paper/2610.00982/) (2026)

## Abstract (arXiv)

Long-horizon robot manipulation remains challenging because similar observations may occur at different execution stages, while the appropriate action depends on previously completed operations. Memory can address this ambiguity by enabling policies to infer task progress from execution history. However, existing memory-augmented approaches often either retain dense histories that require compression or rely primarily on recent context that may discard earlier task-relevant events. In this work, we propose propose KEMO, a lightweight plug-in memory framework that automatically selectively preserves keyframes associated with task-relevant state changes for VLA policies. KEMO combines robot kinematics with visual filtering to detect events, encodes the selected keyframes as compact temporally ordered memory tokens, and integrates them with current visual features through cross-attention and gated residual fusion for VLA training. The detected events also define higher-weight training samples near critical transitions. We evaluate KEMO on various real-world dual-arm manipulation tasks spanning 2 to 6 scored subtasks, and trajectory length ranging from 830 steps to 2846 execution steps (durations from 28 to 95 seconds). Compared with the memory-free baseline (e.g., π0.5), KEMO improves aggregate Task Success Rate by 23.6% and Stage Completion Rate by 34.1%. Ablations show that event-driven keyframe selection outperforms uniform sampling and recent-frame retention, while the proposed gated fusion and keyframe-aligned loss weighting provide complementary gains.