cs.RO · 2026-06-02 · No. 13

Robotics, 2026-06-02.

5 new papers in cs.RO. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

5 entries
  1. 01

    Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics

    Haimin Hu

    cs.RO · cs.AI · cs.LG · eess.SY

    Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate. Safety filters are a popular approach for ensuring safety in interactive robotics, since their modular design separates safety from performance, allowing robots to operate safely around people with minimal impact on task efficiency. While traditional...

    arxiv.org/abs/2606.02562 · PDF

  2. 02

    FW-NKF: Frequency-Weighted Neural Kalman Filters

    Adnan Harun Dogan, Berken Utku Demirel, Christian Holz

    cs.RO · cs.AI · eess.SP

    Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch such as sensor vibrations, electromagnetic interference, and periodic noise. Although Deep Kalman Filter (DKF) variants extend the Extended Kalman Filtering (EKF) framework by learning latent transitions, they lack explicit mechanisms to suppress band-limited noise components that typically...

    arxiv.org/abs/2606.02251 · PDF

  3. 03

    Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

    Youssef Mahran, Zeyad Gamal, Aamir Ahmad, Ayman El-Badawy

    cs.RO · cs.AI · cs.LG

    This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control. Compared to conventional multi-agent MARL formulations that rely on centralized planning or fully decentralized execution, ND-MARL incorporates the swarm communication graph into the decision process. Under a 2-Neighbor communication topology, each agent observes information of only two neighbors and outputs an...

    arxiv.org/abs/2606.02107 · PDF

  4. 04

    World-Task Factorization for Robot Learning

    Eduardo Sebastián, Adrian Pfisterer, Vito Mengers, Oliver Brock, Amanda Prorok

    cs.RO · cs.LG · cs.MA

    Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments. To achieve this, we must structurally factor the policy, which is a choice that dictates what generalizes, what requires retraining, and what remains entangled. Existing methods span a wide spectrum, from expecting structure to emerge from data scaling, to hand-designing it via hierarchies, skill libraries or learned...

    arxiv.org/abs/2606.02027 · PDF

  5. 05

    Learning Action-Conditional and Object-Centric Gaussian Splatting World Models for Rigid Objects

    Jens U. Kreber, Lukas Mack, Joerg Stueckler

    cs.RO · cs.CV · cs.LG

    World models enable intelligent agents to predict the consequences of their actions on the environment. In this paper, we propose Multi Rigid Object Gaussian World Model (MRO-GWM), a novel model that learns action-conditional dynamics of rigid objects in 3D. By representing the scene by object-centric Gaussians, we can represent arbitrary object shapes and multi-object scenes. We develop a novel spatio-temporal transformer architecture that...

    arxiv.org/abs/2606.01950 · PDF

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