Safe Consensus of Cooperative Manipulation with Hierarchical Event-Triggered Control Barrier Functions

• URL: http://arxiv.org/abs/2603.06356v1
• Date: Fri, 06 Mar 2026 15:09:07 GMT
• Title: Safe Consensus of Cooperative Manipulation with Hierarchical Event-Triggered Control Barrier Functions
• Abstract summary: This paper presents a distributed control framework that achieves consensus coordination with safety guarantees via hierarchical event-triggered control barrier functions (CBFs)<n>Results demonstrate higher precision cooperation under strict safety constraints, achieving substantially reduced computational cost and communication frequency compared to baseline methods.
• Score: 3.288997322330816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cooperative transport and manipulation of heavy or bulky payloads by multiple manipulators requires coordinated formation tracking, while simultaneously enforcing strict safety constraints in varying environments with limited communication and real-time computation budgets. This paper presents a distributed control framework that achieves consensus coordination with safety guarantees via hierarchical event-triggered control barrier functions (CBFs). We first develop a consensus-based protocol that relies solely on local neighbor information to enforce both translational and rotational consistency in task space. Building on this coordination layer, we propose a three-level hierarchical event-triggered safety architecture with CBFs, which is integrated with a risk-aware leader selection and smooth switching strategy to reduce online computation. The proposed approach is validated through real-world hardware experiments using two Franka manipulators operating with static obstacles, as well as comprehensive simulations demonstrating scalable multi-arm cooperation with dynamic obstacles. Results demonstrate higher precision cooperation under strict safety constraints, achieving substantially reduced computational cost and communication frequency compared to baseline methods.

Related papers

• Graph-Based Safe Reinforcement Learning for Multi-Agent Systems with Time-Varying Topology [0.0]
  This paper presents a graph-based safe multi-agent reinforcement learning (MARL) framework for cooperative navigation with time-varying topology.<n>To address the critical challenge of ensuring safety in environments with sensing constraints, a safety-decoupled mechanism is introduced.<n>Building upon this safety foundation, a unified structural architecture is proposed, integrating a attention-based actor and a Graph Attention Network (GAT) centralized critic.
  arXiv  Detail & Related papers  (2026-09-08T14:30:06Z)
• MROPE: A Multi-Robot Safe Cooperative Strategy via combined Predictive Safety Filters and Ellipse-based Constraint Compression [2.1769949049404906]
  We propose a hierarchical strategy that decouples the cooperative monitoring mission from strict local safety requirements.<n>Methodologically, this architecture is realized by combining distributed aggregative optimization for high-level swarm coordination.<n>Virtual and real-world experiments validate the framework, demonstrating superior real-time efficiency and scalability.
  arXiv  Detail & Related papers  (2026-07-31T09:22:04Z)
• Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats [76.98547580312116]
  This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs)<n>To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent avoidance.
  arXiv  Detail & Related papers  (2026-07-22T16:37:05Z)
• ADMM-Based Safety-Critical Distributed NMPC for Cooperative Transportation by Quadrupedal Robots [1.7032702581423906]
  This paper presents a safety-critical distributed nonlinear model predictive control (DNMPC) framework for cooperative payload transportation by teams of quadrupedal robots.<n>The proposed approach models the robotic team and the shared payload as a dynamically coupled networked system.<n>The resulting distributed framework enforces consensus over both payload-state and interaction-wrench trajectories.
  arXiv  Detail & Related papers  (2026-07-18T23:44:03Z)
• Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks [48.90302419915548]
  Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in smart factories, warehouses, and service robotics.<n>We propose LDT-Coord, a networked coordination framework built upon a lightweight digital twin (DT)<n>Specifically, each agent independently selects its intended action and reports both the action decision and a structured temporal constraint over shared resources to the DT server.
  arXiv  Detail & Related papers  (2026-07-10T12:10:31Z)
• HCLM: A Hierarchical Framework for Cooperative Loco-Manipulation with Dual Quadrupeds [24.47863400717969]
  We introduce HCLM, a hierarchical framework for general-purpose cooperative loco-manipulation with dual quadrupedal systems.<n>Our architecture systematically decouples high-level collaborative reasoning from low-level robust motion execution.<n>Results demonstrate reliable task execution, strict configurationism, and exceptional resilience against severe physical perturbations.
  arXiv  Detail & Related papers  (2026-05-17T07:23:03Z)
• Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control [50.28263951510334]
  We propose a hybrid hierarchical control framework for reactive dexterous grasping.<n>The proposed framework explicitly decouples high-level spatial intent from low-level joint execution.<n>We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline.
  arXiv  Detail & Related papers  (2026-05-05T04:49:38Z)
• CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment [54.84167973913561]
  CoEnv is a framework that leverages simulation for safe strategy exploration while ensuring reliable real-world deployment.<n>CoEnv operates through three stages: real-to-sim scene reconstruction that digitizes physical workspaces, VLM-driven action synthesis supporting both real-time planning with code-based trajectory generation, and validated sim-to-real transfer with collision detection for safe deployment.
  arXiv  Detail & Related papers  (2026-04-07T06:24:41Z)
• BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
  BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
  arXiv  Detail & Related papers  (2026-02-23T18:19:46Z)
• CTHA: Constrained Temporal Hierarchical Architecture for Stable Multi-Agent LLM Systems [0.0]
  Multi-time-scale agent architectures have extended the ubiquitous single-loop paradigm by introducing temporal hierarchies with distinct cognitive layers.<n>We propose Constrained Temporal Hierarchical Architecture (CTHA) to restore coordination stability, while incorporating principled arbitration mechanisms to ensure coherent decision-making.<n>CTHA is effective for complex task execution at scale, offering 47% reduction in failure cascades, 2.3x improvement in sample efficiency, and superior scalability compared to unconstrained hierarchical baselines.
  arXiv  Detail & Related papers  (2026-01-09T08:03:14Z)
• QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management [52.15690855486153]
  A space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity.<n>This paper formulates UAV mobility management in SAGIN as a constrained multiobjective joint optimization problem.
  arXiv  Detail & Related papers  (2025-12-17T06:22:46Z)
• Deep Reinforcement Learning for Multi-Agent Coordination [8.250169938213558]
  We propose a Stigmergic Multi-Agent Deep Reinforcement Learning (S-MADRL) framework that leverages virtual pheromones to model local and social interactions.<n>We show that our framework achieves the most effective coordination of up to eight agents, where robots self-organize into asymmetric workload distributions.<n>This emergent behavior, analogous to strategies observed in nature, demonstrates a scalable solution for decentralized multi-agent coordination in crowded environments.
  arXiv  Detail & Related papers  (2025-10-04T00:47:20Z)
• COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping [56.907940167333656]
  Occluded robot grasping is where the desired grasp poses are kinematically infeasible due to environmental constraints such as surface collisions.<n>Traditional robot manipulation approaches struggle with the complexity of non-prehensile or bimanual strategies commonly used by humans.<n>We introduce Constraint-based Manipulation for Bimanual Occluded Grasping (COMBO-Grasp), a learning-based approach which leverages two coordinated policies.
  arXiv  Detail & Related papers  (2025-02-12T01:31:01Z)
• Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraints [50.920465513162334]
  We introduce the PCBF algorithm to maximize the inner safe set of dynamical systems under input constraints.<n>We validate its effectiveness through comparison with Hamilton-Jacobi reachability for an inverted pendulum and through simulations on a 12-dimensional quadrotor system.<n>Results show that the PCBF consistently outperforms existing methods, yielding larger safe sets and ensuring safety under input constraints.
  arXiv  Detail & Related papers  (2024-10-05T18:45:19Z)
• Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks [0.24578723416255746]
  In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability. We propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy.
  arXiv  Detail & Related papers  (2024-02-04T15:54:03Z)
• Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions [58.19198103790931]
  This paper addresses the problem of safety-critical control for non-affine control systems. It has been shown that optimizing quadratic costs subject to state and control constraints can be sub-optimally reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs) We incorporate higher-order CBFs into neural ordinary differential equation-based learning models as differentiable CBFs to guarantee safety for non-affine control systems.
  arXiv  Detail & Related papers  (2023-09-06T05:35:48Z)
• Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios [10.37986799561165]
  Communication technologies enable coordination among connected and autonomous vehicles (CAVs) We propose a framework of constrained multi-agent reinforcement learning (MARL) with a parallel safety shield for CAVs. Results show that our proposed methodology significantly increases system safety and efficiency in challenging scenarios.
  arXiv  Detail & Related papers  (2022-10-05T14:39:07Z)
• Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport [9.891241465396098]
  We explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for cooperative transport. Our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again.
  arXiv  Detail & Related papers  (2021-03-29T01:16:12Z)

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.