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

• URL: http://arxiv.org/abs/2606.23589v1
• Date: Mon, 22 Jun 2026 16:57:43 GMT
• Title: KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies
• Abstract summary: Memory can address this ambiguity by enabling policies to infer task progress from execution history.<n>We propose KEMO, a lightweight plug-in memory framework that automatically preserves kinematics associated with task-relevant state changes for VLA policies.<n>We evaluate KEMO on various real-world dualarm 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)
• Score: 14.253600207124194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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., $π_{0.5}$), 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.

Related papers

• TFP: Temporally Conditioned Memory-Fusion Policies for Visuomotor Learning [11.149498165285491]
  Vision--Language--Action (VLA) policies such as $_0.5$ and OpenVLA perform well on many tasks, but they are often reactive.<n>We argue that such tasks require not only memory, but dynamics-aware belief updates.
  arXiv  Detail & Related papers  (2026-07-09T09:24:30Z)
• EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies [68.812675280427]
  EventVLA is an end-to-end framework founded on the concept of sparse visual evidence memory.<n>KEM directly predicts future probabilities from the VLA's latent embeddings to autonomously capture and store sparse, task-critical visual events.<n>We propose RoboTwin-MeM, a diagnostic benchmark designed to evaluate non-Markovian manipulation tasks with interactive visual evidence.
  arXiv  Detail & Related papers  (2026-06-18T11:11:37Z)
• Scaling Short-Term Memory of Visuomotor Policies for Long-Horizon Tasks [69.19366746169906]
  We present PRISM, a transformer-based architecture for visuomotor policies to use short-term memory.<n>Piracy filters retrieved information to suppress irrelevant details, improving performance.<n> hierarchical architecture compresses local information into compact tokens, improving its compute and memory footprint.
  arXiv  Detail & Related papers  (2026-06-15T03:49:23Z)
• Planning-aligned Token Compression for Long-Context Autonomous Driving [95.59023657139208]
  We propose a planning-aligned working memory framework built on conditional VQ-VA.<n>Compression is conditioned on both historical trajectory and a learned planning intent.<n>We evaluate on high-signal dynamic scenarios where historical context is most critical for behavior correctness.
  arXiv  Detail & Related papers  (2026-06-05T17:16:21Z)
• VPWEM: Non-Markovian Visuomotor Policy with Working and Episodic Memory [31.464584758455356]
  VPWEM is a non-Markovian visuomotor policy equipped with working and episodic memories.<n>It exploits both short-term and episode-wide information for action generation with nearly constant memory and computation per step.
  arXiv  Detail & Related papers  (2026-03-05T07:52:50Z)
• RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies [54.23445842621374]
  Memory is critical for long-horizon and history-dependent robotic manipulation.<n>Recent vision-language-action (VLA) models have begun to incorporate memory mechanisms.<n>We introduce RoboMME: a large-scale standardized benchmark for evaluating and advancing VLA models.
  arXiv  Detail & Related papers  (2026-03-04T21:59:32Z)
• Non-Markovian Long-Horizon Robot Manipulation via Keyframe Chaining [56.62125584296097]
  Keyframe-Chaining VLA is a framework that extracts and links key historical frames to model long-horizon dependencies.<n>We design a progress-aware mechanism that dynamically retrieves historical frames based on their temporal relevance to the current execution phase.<n>We introduce a suite of four Non-Markovian manipulation tasks built upon the ManiSkill simulator to measure task success rates.
  arXiv  Detail & Related papers  (2026-03-02T05:26:29Z)
• Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding [12.400431805327093]
  PAM is a novel visuomotor policy equipped with Adaptive working Memory.<n>It supports a 300-frame history window while maintaining high inference speed.<n>We meticulously design 7 tasks and verify that PAM can handle multiple scenarios of state ambiguity simultaneously.
  arXiv  Detail & Related papers  (2025-12-31T05:20:13Z)
• CronusVLA: Towards Efficient and Robust Manipulation via Multi-Frame Vision-Language-Action Modeling [84.51372201195132]
  CronusVLA is a unified framework that extends single-frame VLA models to the multi-frame paradigm.<n>CronusVLA achieves leading performance and superior robustness, with a 70.9% success rate.<n>These results highlight the potential of efficient multi-frame adaptation in VLA models for more powerful and robust real-world deployment.
  arXiv  Detail & Related papers  (2025-06-24T17:30:27Z)
• Activation-aware Probe-Query: Effective Key-Value Retrieval for Long-Context LLMs Inference [56.71209737306054]
  We propose textbfActQKV, a training-free, textbfActivation-aware approach that dynamically determines probe-textbfQuery and leverages it to retrieve the relevant textbfKV pairs for inference.<n>Experiments on the Long-Bench and $infty$ Benchmarks demonstrate its state-of-the-art performance with competitive inference quality and resource efficiency.
  arXiv  Detail & Related papers  (2025-02-19T08:50:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.