Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations
- URL: http://arxiv.org/abs/2605.21901v1
- Date: Thu, 21 May 2026 02:22:09 GMT
- Title: Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations
- Abstract summary: In communicationless environments, multi-robot systems must operate without the constant information exchange that many coordination strategies typically assume.<n>This paper presents a novel dynamic epistemic planning framework that enables implicit coordination and long horizon planning through higher-order reasoning among robots.
- Score: 4.239775815863115
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In communicationless environments, multi-robot systems must operate without the constant information exchange that many coordination strategies typically assume. This paper presents a novel dynamic epistemic planning framework that enables implicit coordination and long horizon planning through higher-order reasoning among robots. With our approach, robots form and propagate higher-order belief particles, update world beliefs using Bayesian inference, and select actions via a behavior tree that anticipates teammates' likely decisions. A temporally aware Model Predictive Path Integral (MPPI) controller integrates this reasoning into low-level execution, allowing robots to plan intercepts and adapt trajectories under partial observability. The proposed framework is evaluated in both simulations and physical experiments, where it consistently reduces task completion time compared to a first-order baseline, demonstrating that epistemic logic can serve as a robust foundation for resilient coordination in communication-restricted domains.
Related papers
- From Real-Time Planning to Reliable Execution:Scalable Coordination for Heterogeneous Multi-Robot Fleets in Industrial Environments [1.4860612474543247]
This paper presents a reactive online coordination framework that enables real-time planning while maintaining robust execution.<n>We introduce a motion-induced conflict reduction mechanism to support the online generation of feasible paths for online conflict resolution.<n>To mitigate the effects of disturbances, we further design a generalized Conjugate Action-Precedence Hypergraph.
arXiv Detail & Related papers (2026-07-01T08:16:19Z) - VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands [56.029505206531155]
Vision Guided Agile Interaction Control (VAIC) is a unified framework that bridges the gap between current controllers and humanoid robots.<n>First, a privileged teacher policy masters diverse interaction skills using precise object kinematics and exact environmental states.<n>Second, a deployable student policy distills these capabilities by replacing full body tracking with velocity targets across multiple axes.
arXiv Detail & Related papers (2026-06-08T09:52:55Z) - Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
arXiv Detail & Related papers (2026-05-26T21:17:53Z) - ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation [9.708461585583791]
Collaborative transportation of heavy payloads via loco-manipulation is a challenging yet essential capability for legged robots.<n>This study proposes an Alternating Direction Method of Multipliers-based distributed model predictive control framework for loco-manipulation.
arXiv Detail & Related papers (2026-03-07T08:06:51Z) - Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction [57.252599851031505]
We introduce Perceptive Root-guided Humanoid-Object Interaction, Pro-HOI.<n>Pro-HOI is a generalizable framework for robust humanoid loco-manipulation.<n> Empirical validation on a Unitree G1 robot demonstrates that Pro-HOI significantly outperforms baselines in generalization and robustness.
arXiv Detail & Related papers (2026-03-01T14:19:59Z) - Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning.
We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures.
We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
arXiv Detail & Related papers (2024-01-22T18:58:22Z) - LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [72.81786007015471]
We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem.<n>CNN processes localized perception; a graph neural network (GNN) facilitates robot communications.<n> Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
arXiv Detail & Related papers (2024-01-10T00:08:00Z) - Epistemic Prediction and Planning with Implicit Coordination for
Multi-Robot Teams in Communication Restricted Environments [2.781492199939609]
In communication restricted environments, a multi-robot system can be deployed to either: i. maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii. allow disconnections to increase environmental coverage efficiency, challenges on how, when, and where to reconnect (rendezvous problem)
This paper proposes a coordinated prediction and planning framework to achieve consensus without communicating for exploration and coverage, task discovery and completion, and rendezvous applications.
arXiv Detail & Related papers (2023-02-21T01:52:21Z) - HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and
Sensing [1.3678064890824186]
The Human Assisted Robotic Planning and Sensing (HARPS) framework is presented for active semantic sensing and planning in human-robot teams.
This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments.
Simulations of a UAV-enabled target search application in a large-scale partially structured environment show significant improvements in time and belief state estimates.
arXiv Detail & Related papers (2021-10-20T00:41:57Z) - SABER: Data-Driven Motion Planner for Autonomously Navigating
Heterogeneous Robots [112.2491765424719]
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal.
We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints.
recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution.
A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
arXiv Detail & Related papers (2021-08-03T02:56:21Z) - Leveraging Neural Network Gradients within Trajectory Optimization for
Proactive Human-Robot Interactions [32.57882479132015]
We present a framework that fuses together the interpretability and flexibility of trajectory optimization (TO) with the predictive power of state-of-the-art human trajectory prediction models.
We demonstrate the efficacy of our approach in a multi-agent scenario whereby a robot is required to safely and efficiently navigate through a crowd of up to ten pedestrians.
arXiv Detail & Related papers (2020-12-02T08:43:36Z) - iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on
Robots [46.13039152809055]
We present a novel algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers.
Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines.
arXiv Detail & Related papers (2020-04-18T17:46:59Z)
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.