A system developed by researchers at the University of Waterloo lets people collaborate with groups of robots to create works of art inspired by music. The new technology features multiple wheeled robots about the size of soccer balls that trail coloured light as they move within a fixed area on the floor in response to key features of music including tempo and chord progression. A camera records the co-ordinated light trails as they snake within that area, which serves as the canvas for the creation of a "painting," or visual representation of the emotional content of a particular piece of music. "Basically, we programmed a swarm of robots to paint based on musical input," said Dr Gennaro Notomista, a professor of electrical and computer engineering at Waterloo. "The result is a cohesive system that not only processes musical input, but also co-ordinates multiple painting robots to create adaptive, expressive art that reflects the emotional essence of the music being played."

Gestures are an expressive input modality for controlling multiple robots, but their use is often limited by rigid mappings and recognition constraints. To move beyond these limitations, we propose roleplaying metaphors as a scaffold for designing richer interactions. By introducing three roles: Director, Puppeteer, and Wizard, we demonstrate how narrative framing can guide the creation of diverse gesture sets and interaction styles. These roles enable a variety of scenarios, showing how roleplay can unlock new possibilities for multi-robot systems. Our approach emphasizes creativity, expressiveness, and intuitiveness as key elements for future human-robot interaction design.

-- Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition. Existing methods typically rely on prior environmental knowledge or carefully designed task-specific prompts, making them struggle with dynamic scene changes or unexpected task conditions, e.g., a robot attempting to put a carrot in the microwave but finds the door was closed. Such challenges underscore two critical issues: adaptability and efficiency. T o address them, in this work, we propose an adaptive multi-agent planning framework, termed REMAC, that enables efficient, scene-agnostic multi-robot long-horizon task planning and execution through continuous reflection and self-evolution. REMAC incorporates two key modules: a self-reflection module performing pre-condition and post-condition checks in the loop to evaluate progress and refine plans, and a self-evolvement module dynamically adapting plans based on scene-specific reasoning. It offers several appealing benefits: 1) Robots can initially explore and reason about the environment without complex prompt design. T o validate REMAC's effectiveness, we build a multi-agent environment for long-horizon robot manipulation and navigation based on RoboCasa, featuring 4 task categories with 27 task styles and 50+ different objects. Based on it, we further benchmark state-of-the-art reasoning models, including DeepSeek-R1, o3-mini, QwQ, and Grok3, demonstrating REMAC's superiority by boosting average success rates by 40% and execution efficiency by 52.7% over the single robot baseline without any task-specific prompting or finetuning. In recent years, Vision-Language Models (VLMs) have seen significant application in robot control tasks [1, 2].

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

In many industrial robotics applications, multiple robots are working in a shared workspace to complete a set of tasks as quickly as possible. Such settings can be treated as multi-modal multi-robot multi-goal path planning problems, where each robot has to reach an ordered sequence of goals. Existing approaches to this type of problem solve this using prioritization or assume synchronous completion of tasks, and are thus neither optimal nor complete. We formalize this problem as a single path planning problem and introduce a benchmark encompassing a diverse range of problem instances including scenarios with various robots, planning horizons, and collaborative tasks such as handovers. Along with the benchmark, we adapt an RRT* and a PRM* planner to serve as a baseline for the planning problems. Both planners work in the composite space of all robots and introduce the required changes to work in our setting. Unlike existing approaches, our planner and formulation is not restricted to discretized 2D workspaces, supports a changing environment, and works for heterogeneous robot teams over multiple modes with different constraints, and multiple goals. Videos and code for the benchmark and the planners is available at https://vhartman.github.io/mrmg-planning/.

For tasks conducted in unknown environments with efficiency requirements, real-time navigation of multi-robot systems remains challenging due to unfamiliarity with surroundings.In this paper, we propose a novel multi-robot collaborative planning method that leverages the perception of different robots to intelligently select search directions and improve planning efficiency. Specifically, a foundational planner is employed to ensure reliable exploration towards targets in unknown environments and we introduce Graph Attention Architecture with Information Gain Weight(GIWT) to synthesizes the information from the target robot and its teammates to facilitate effective navigation around obstacles.In GIWT, after regionally encoding the relative positions of the robots along with their perceptual features, we compute the shared attention scores and incorporate the information gain obtained from neighboring robots as a supplementary weight. We design a corresponding expert data generation scheme to simulate real-world decision-making conditions for network training. Simulation experiments and real robot tests demonstrates that the proposed method significantly improves efficiency and enables collaborative planning for multiple robots. Our method achieves approximately 82% accuracy on the expert dataset and reduces the average path length by about 8% and 6% across two types of tasks compared to the fundamental planner in ROS tests, and a path length reduction of over 6% in real-world experiments.

This paper presents a game theoretic formulation of a graph traversal problem, with applications to robots moving through hazardous environments in the presence of an adversary, as in military and security applications. The blue team of robots moves in an environment modeled by a time-varying graph, attempting to reach some goal with minimum cost, while the red team controls how the graph changes to maximize the cost. The problem is formulated as a stochastic game, so that Nash equilibrium strategies can be computed numerically. Bounds are provided for the game value, with a guarantee that it solves the original problem. Numerical simulations demonstrate the results and the effectiveness of this method, particularly showing the benefit of mixing actions for both players, as well as beneficial coordinated behavior, where blue robots split up and/or synchronize to traverse risky edges.

This work presents a use case of federated learning (FL) applied to discovering a maze with LiDAR sensors-equipped robots. Goal here is to train classification models to accurately identify the shapes of grid areas within two different square mazes made up with irregular shaped walls. Due to the use of different shapes for the walls, a classification model trained in one maze that captures its structure does not generalize for the other. This issue is resolved by adopting FL framework between the robots that explore only one maze so that the collective knowledge allows them to operate accurately in the unseen maze. This illustrates the effectiveness of FL in real-world applications in terms of enhancing classification accuracy and robustness in maze discovery tasks.

In recent years, the widespread application of multi-robot systems in areas such as power inspection, autonomous vehicle fleets has made multi-robot technology a research hotspot in the field of robotics. This paper investigates multi-robot cooperative exploration in unknown environments, proposing a training framework and decision strategy based on multi-agent reinforcement learning. Specifically we propose a Asymmetric Topological Representation based mapping framework (ATR-Mapping), combining the advantages of methods based on raw grid maps and methods based on topology, the structural information from the raw grid maps is extracted and combined with a topological graph constructed based on geometric distance information for decision-making. Leveraging this topological graph representation, we employs a decision network based on topological graph matching to assign corresponding boundary points to each robot as long-term target points for decision-making. We conducts testing and application of the proposed algorithms in real world scenarios using the Gazebo and Gibson simulation environments. It validates that the proposed method, when compared to existing methods, achieves a certain degree of performance improvement.

In autonomous robotics, a critical challenge lies in developing robust solutions for Active Collaborative SLAM, wherein multiple robots collaboratively explore and map an unknown environment while intelligently coordinating their movements and sensor data acquisitions. In this article, we present two approaches for coordinating a system consisting of multiple robots to perform Active Collaborative SLAM (AC-SLAM) for environmental exploration. Our two coordination approaches, synchronous and asynchronous implement a methodology to prioritize robot goal assignments by the central server. We also present a method to efficiently spread the robots for maximum exploration while keeping SLAM uncertainty low. Both coordination approaches were evaluated through simulation and experiments on publicly available datasets, rendering promising results.