model predictive control
PulseAugur coverage of model predictive control — every cluster mentioning model predictive control across labs, papers, and developer communities, ranked by signal.
9 day(s) with sentiment data
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Robotic peg insertion enhanced by learned contact dynamics model
Researchers have developed a novel graph neural network model capable of learning contact dynamics for robotic manipulation tasks like peg insertion. This model, trained through self-supervision using only touch and for…
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New backdoor attack targets AI world models for control systems
Researchers have identified a new supply-chain vulnerability in pretrained world models, which are used as simulators for control tasks. An adversary can embed a backdoor in a model checkpoint, allowing them to hijack d…
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New variable horizon method enhances multi-drone collision avoidance
Researchers have developed a novel conflict-predictive variable horizon approach for multi-drone distributed model predictive control. This method allows each drone to dynamically adjust its prediction horizon based on …
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New transfer learning model enhances building control performance
Researchers have developed a novel zero-shot transfer learning approach for model predictive control (MPC) in buildings. This method utilizes generalized models pretrained on excitation-based operational data, which pur…
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New research compares control strategies for sustainable hydrogen supply chains
Researchers have developed and compared four control approaches for optimizing renewable-powered hydrogen supply chains. Model Predictive Control (MPC) demonstrated the highest economic performance by utilizing short-te…
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New MPC framework uses Gaussian Processes for robust control
Researchers have developed a new model predictive control (MPC) framework designed for uncertain nonlinear systems. This framework utilizes Gaussian Processes (GPs) to learn system dynamics from noisy measurements, inco…
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New Q-SVMPC method enhances trajectory optimization with RL and SVGD
Researchers have developed Q-SVMPC, a novel approach to model predictive control (MPC) that leverages Q-learning and Stein Variational Gradient Descent (SVGD) to enhance trajectory optimization. This method aims to over…
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LLMs struggle with HVAC deployment due to data and safety issues
A review of 66 studies on Large Language Models (LLMs) for HVAC operations reveals significant challenges in deploying these agents in building automation systems. The primary hurdles include normalizing heterogeneous s…
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LLMs show promise for HVAC but aren't ready for industry adoption
A recent review of 66 studies published between 2023 and March 2026 indicates that while large language models (LLMs) show promise for HVAC operations in building energy systems, they are not yet ready for widespread in…
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Model Predictive Control Optimizes Heterogeneous Restless Multi-armed Bandits
Researchers have developed a new approach using Model Predictive Control (MPC) to optimize heterogeneous restless multi-armed bandits (RMABs). This method, termed the LP-update policy, repeatedly solves finite-horizon l…
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New SUN Programs framework unifies control and learning for robotic manipulation
Researchers have developed SUN Programs, a novel framework that unifies model-based control and learned policies for complex manipulation tasks. This system, named Kuafu, automatically synthesizes these programs from la…
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New decentralized MPC framework ensures safety in multi-agent systems
Researchers have developed a new decentralized model predictive control (MPC) framework for multi-agent systems that operates under state-only information and limited sensing. This approach ensures recursive feasibility…
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AI framework enhances border control with real-time queue prediction
Researchers have developed a novel multi-modal AI framework designed to enhance border control systems through real-time queue prediction and management. This framework integrates diverse data sources, utilizing Long Sh…
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New research explores robust, adaptive, and structured reinforcement learning techniques · 10 sources tracked
Multiple research papers published on arXiv explore advancements in reinforcement learning (RL) techniques. One paper unifies regularization-based methods for robust deep RL against adversarial perturbations, proposing …
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New DRL-MPC framework enhances control of multi-class transportation networks
Researchers have developed a novel framework that integrates Deep Reinforcement Learning (DRL) with Model Predictive Control (MPC) to manage complex multi-class transportation networks. This hybrid approach aims to over…
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New SRL-MPC method enables safe robot navigation in complex crowds
Researchers have developed a new method called Shape-Aware Reinforcement Learned Model Predictive Control (SRL-MPC) to address the challenges of safe and efficient navigation for robots in heterogeneous crowds. This app…
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New methods enhance AI planning with latent world models · 4 sources tracked
Researchers have developed new methods to improve planning in latent world models, which are systems that predict outcomes of action sequences. One approach, Reinforced Planning (RP1), learns to improve multi-step plans…
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AI Planning Faces Uncertainty: New Article Proposes Robust Strategies
A new article discusses the challenges of planning with learned models, emphasizing that any model used for planning is inherently imperfect. It proposes strategies for robust planning under uncertainty, such as Model P…
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New STEER2REACH method improves Hamilton-Jacobi reachability analysis
Researchers have developed STEER2REACH (S2R), a new method for solving Hamilton-Jacobi (HJ) reachability problems. This approach utilizes physics-informed neural networks (PINNs) and an adaptive sampling distribution th…
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New DPC method offers deterministic safety guarantees for control systems
Researchers have developed a novel method for Differentiable Predictive Control (DPC) that provides deterministic feasibility guarantees, a critical aspect for safe control systems. This approach leverages topological a…