Event-Triggered Control

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VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon
Jul 2, 2026Yi Pan, Miao Pan, Qi Lu +8Long-Horizon Robotic Manipulation Feedback Control
Zhejiang University · Alibaba DAMO Academy
Deep Reinforcement Learning-Enhanced Event-Triggered Data-Driven Predictive Control for a 3D Cable-Driven Soft Robotic Arm
Jun 24, 2026Cheng Ouyang, Moeen Ul Islam, Kaixiang Zhang +3RL Control Robot Trajectory Tracking
Department of Agricultural & Biological Engineering, Mississippi State University, MS, USA · Department of Mechanical Engineering, Michigan State University, MI, USA · Department of Electrical and Computer Engineering, Michigan State University, MI, USA
SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents
May 18, 2026Wencan Jiang, Jiangning Zhang, Jianbiao Mei +6Game-Playing Agents Feedback Control
1Zhejiang University · 2National University of Singapore · 3Nanyang Technological University
Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
May 11, 2026Qingyun Guo, Junyi Shi, Tomasz Piotr Kucner +1Agent Communication Protocols Feedback Control
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland · Center for Artificial Intelligence, Helsinki, Finland
When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
May 7, 2026Yi Wang, Chandrajit BajajDynamical Systems Learning to Optimize
1Oden Institute · Department of Computer Science
Bio-plausible Neuromorphic Disturbance Observer Based on Emulation Theory: Extended Version
May 5, 2026Hongfu Xu, Xiaoyu Guo, Shengbo Wang +1Robust Control Spiking Neural Networks
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China · Department of Mechanical Engineering, City University of Hong Kong, Hong Kong · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong +1
Neural Network-Based Adaptive Event-Triggered Control for Dual-Arm Unmanned Aerial Manipulator Systems
Apr 18, 2026Yang Wang, Hai Yu, Wei He +3Robot Trajectory Tracking Aerial Robotics
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China. · Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
Safe Consensus of Cooperative Manipulation with Hierarchical Event-Triggered Control Barrier Functions
Mar 6, 2026Simiao Zhuang, Bingkun Huang, Zewen YangControl Barrier Functions Multi-Agent Control
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), 80992 Munich, Germany
Distributed Event-Triggered Distance-Based Formation Control for Multi-Agent Systems
Sep 15, 2025Evangelos Psomiadis, Panagiotis TsiotrasMulti-Robot Formation Control Multi-Agent Control
D. Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0150, USA

Jul 2, 2026, cs.ROJ/K move · Enter open · S save

VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon

Yi Pan, Miao Pan, Qi Lu, Jiaming Huang+7

Zhejiang University · Alibaba DAMO Academy

Long-Horizon Robotic Manipulation Feedback Control Action Chunking Event-Triggered Control Robustness of VLA Models

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Vision-Language-Action (VLA) foundation models have recently achieved strong progress in embodied intelligence. To reduce policy-call frequency while preserving temporal coherence, most generative policies adopt an action chunk mechanism, executing multiple future actions in an open-loop manner under a fixed action horizon. However, this "predict-then-blindly-execute" paradigm sacrifices closed-loop reactivity: in contact-rich physical interactions, even small local perturbations can rapidly amplify within the open-loop blind spot, leading to compounding errors and ultimately task failure. To address this limitation, we propose VLA-Corrector, a lightweight corrective inference framework for action-chunked VLA policies. Without modifying the backbone policy weights, VLA-Corrector introduces a lightweight Latent-space Vision Monitor (LVM) that continuously compares predicted and actual visual feature evolution, enabling online detection of visual dynamics deviations. Once persistent deviation is detected, the system triggers a truncation event, discards the remaining stale actions, and invokes corrective replanning via Online Gradient Guidance (OGG). The detect-and-correct mechanism of VLA-Corrector naturally induces an event-triggered adaptive action horizon: it preserves long-horizon execution when the current chunk remains reliable, and invokes short-horizon corrective replanning when execution begins to drift. In doing so, VLA-Corrector mitigates the trade-off imposed by static horizons between execution robustness and policy-call frequency. It can be integrated into different VLA models without further retraining the VLA backbone, interrupting compounding errors while preserving much of the efficiency benefit of action chunking and substantially improving robustness in long-horizon, contact-rich robotic manipulation tasks.

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