The key ingredient to MixTURE's success is automatically learning

Extending recent advances in Learning from Demonstration (LfD) frameworks to multi-robot settings poses critical challenges such as environment non-stationarity due to partial observability which is detrimental to the applicability of existing methods. Although prior work has shown that enabling communication among agents of a robot team can alleviate such issues, creating inter-agent communication under existing Multi-Agent LfD (MA-LfD) frameworks requires the human expert to provide demonstrations for both environment actions and communication actions, which necessitates an efficient communication strategy on a known message spaces. To address this problem, we propose Mixed-Initiative Multi-Agent Apprenticeship Learning (MixTURE). MixTURE enables robot teams to learn from a human expert-generated data a preferred policy to accomplish a collaborative task, while simultaneously learning emergent inter-agent communication to enhance team coordination. The key ingredient to MixTURE's success is automatically learning a communication policy, enhanced by a mutual-information maximizing reverse model that rationalizes the underlying expert demonstrations without the need for human generated data or an auxiliary reward function. MixTURE outperforms a variety of relevant baselines on diverse data generated by human experts in complex heterogeneous domains. MixTURE is the first MA-LfD framework to enable learning multi-robot collaborative policies directly from real human data, resulting in ~44% less human workload, and ~46% higher usability score.

The key ingredient to MixTURE's success is automatically learning

Autonomous exploration and object search in unknown indoor environments remain challenging for multi-robot systems (MRS). Traditional approaches often rely on greedy frontier assignment strategies with limited inter-robot coordination. In this work, we introduce LLM-MCoX (LLM-based Multi-robot Coordinated Exploration and Search), a novel framework that leverages Large Language Models (LLMs) for intelligent coordination of both homogeneous and heterogeneous robot teams tasked with efficient exploration and target object search. Our approach combines real-time LiDAR scan processing for frontier cluster extraction and doorway detection with multimodal LLM reasoning (e.g., GPT-4o) to generate coordinated waypoint assignments based on shared environment maps and robot states. LLM-MCoX demonstrates superior performance compared to existing methods, including greedy and Voronoi-based planners, achieving 22.7% faster exploration times and 50% improved search efficiency in large environments with 6 robots. Notably, LLM-MCoX enables natural language-based object search capabilities, allowing human operators to provide high-level semantic guidance that traditional algorithms cannot interpret.

Coordinated multi-robot navigation is essential for robots to operate as a team in diverse environments. During navigation, robot teams usually need to maintain specific formations, such as circular formations to protect human teammates at the center. However, in complex scenarios such as narrow corridors, rigidly preserving predefined formations can become infeasible. Therefore, robot teams must be capable of dynamically splitting into smaller subteams and adaptively controlling the subteams to navigate through such scenarios while preserving formations. To enable this capability, we introduce a novel method for SubTeaming and Adaptive Formation (STAF), which is built upon a unified hierarchical learning framework: (1) high-level deep graph cut for team splitting, (2) intermediate-level graph learning for facilitating coordinated navigation among subteams, and (3) low-level policy learning for controlling individual mobile robots to reach their goal positions while avoiding collisions. To evaluate STAF, we conducted extensive experiments in both indoor and outdoor environments using robotics simulations and physical robot teams. Experimental results show that STAF enables the novel capability for subteaming and adaptive formation control, and achieves promising performance in coordinated multi-robot navigation through challenging scenarios. More details are available on the project website: https://hcrlab.gitlab.io/project/STAF.

Multi-robot coordination is crucial for autonomous systems, yet real-world deployments often encounter various failures. These include both temporary and permanent disruptions in sensing and communication, which can significantly degrade system robustness and performance if not explicitly modeled. Despite its practical importance, failure-aware coordination remains underexplored in the literature. To bridge the gap between idealized conditions and the complexities of real-world environments, we propose a unified failure-aware coordination framework designed to enable resilient and adaptive multi-robot target tracking under both temporary and permanent failure conditions. Our approach systematically distinguishes between two classes of failures: (1) probabilistic and temporary disruptions, where robots recover from intermittent sensing or communication losses by dynamically adapting paths and avoiding inferred danger zones, and (2) permanent failures, where robots lose sensing or communication capabilities irreversibly, requiring sustained, decentralized behavioral adaptation. To handle these scenarios, the robot team is partitioned into subgroups. Robots that remain connected form a communication group and collaboratively plan using partially centralized nonlinear optimization. Robots experiencing permanent disconnection or failure continue to operate independently through decentralized or individual optimization, allowing them to contribute to the task within their local context. We extensively evaluate our method across a range of benchmark variations and conduct a comprehensive assessment under diverse real-world failure scenarios. Results show that our framework consistently achieves robust performance in realistic environments with unknown danger zones, offering a practical and generalizable solution for the multi-robot systems community.

-- In disaster response or surveillance operations, quickly identifying areas needing urgent attention is critical, but deploying response teams to every location is inefficient or often impossible. Effective performance in this domain requires coordinating a multi-robot inspection team to prioritize inspecting locations more likely to need immediate response, while also minimizing travel time. This is particularly challenging because robots must directly observe the locations to determine which ones require additional attention. This work introduces a multi-robot planning framework for coordinated time-critical multi-robot search under uncertainty. Our approach uses a graph neural network to estimate the likelihood of PoIs needing attention from noisy sensor data and then uses those predictions to guide a multi-robot model-based planner to determine the cost-effective plan. Simulated experiments demonstrate that our planner improves performance at least by 16.3%, 26.7%, and 26.2% for 1, 3, and 5 robots, respectively, compared to non-learned and learned baselines. In scenarios like disaster aftermath inspection or critical surveillance operations, quickly traveling to and inspecting affected areas is crucial for an efficient response.

The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can result in estimation errors that impact collaborative efforts among robots. In this paper, we propose a new metric termed Loss of Information Utility (LoIU) to quantify the freshness and utility of information critical for cooperation. The metric enables robots to prioritize information transmissions within bandwidth constraints. We also propose the estimation of LoIU using belief distributions and accordingly optimize both transmission schedule and resource allocation strategy for device-to-device transmissions to minimize the time-average LoIU within a robot team. A semi-decentralized Multi-Agent Deep Deterministic Policy Gradient framework is developed, where each robot functions as an actor responsible for scheduling transmissions among its collaborators while a central critic periodically evaluates and refines the actors in response to mobility and interference. Simulations validate the effectiveness of our approach, demonstrating an enhancement of information freshness and utility by 98%, compared to alternative methods.

Advancements in generative models have enabled multi-agent systems (MAS) to perform complex virtual tasks such as writing and code generation, which do not generalize well to physical multi-agent robotic teams. Current frameworks often treat agents as conceptual task executors rather than physically embodied entities, and overlook critical real-world constraints such as spatial context, robotic capabilities (e.g., sensing and navigation). To probe this gap, we reconfigure and stress-test a hierarchical multi-agent robotic team built on the CrewAI framework in a simulated emergency department onboarding scenario. We identify five persistent failure modes: role misalignment; tool access violations; lack of in-time handling of failure reports; noncompliance with prescribed workflows; bypassing or false reporting of task completion. Based on this analysis, we propose three design guidelines emphasizing process transparency, proactive failure recovery, and contextual grounding. Our work informs the development of more resilient and robust multi-agent robotic systems (MARS), including opportunities to extend virtual multi-agent frameworks to the real world.

Claire chatted to Tanja Katharina Kaiser from the University of Technology Nuremberg about how applying evolutionary principles can help robot teams make better decisions. Tanja Katharina Kaiser is a senior researcher heading the Multi-Robot Systems Satellite Lab at the University of Technology Nuremberg (UTN) in Germany. She and her team focus on the development of adaptive multi-robot systems to solve complex real-world tasks using artificial intelligence. Tanja received her doctorate in robotics from the University of Lübeck in Germany in 2022. Before joining UTN, she held postdoctoral research positions at the Technical University of Dresden and the University of Konstanz.