Abstract Significant advances in robot sensing and mobility have enabled the use of multi-robot networks to track multiple targets moving in an environment. Tracking moving targets that outnumber the size of the robot network involves the network to collaboratively assume a subset of targets to be tracked and plan the actions of the robots. This paper focuses on the scenario when the targets outnumber the robots in the network. The goal of the network is to assign targets to the robots and plan robot actions to track these assigned targets. This has to be done consistently as the robots and targets move in the environment to ensure the tracking performance is maximized. However, this problem, as shown in this paper, is NP-hard. This paper leverages decomposition theory to solve this problem efficiently in real-time in two stages. The first stage leverages inter-robot communication in the network to assign robots to targets, and the second stage, solved on each robot locally, optimizes the control to track the targets assigned to it. A novel decentralized approach, called bundle-based assignment, is presented to find adaptive and conflict-free target assignment in the first stage that guarantees $$\frac{1}{2}$$ 1 2 -approximation in the worst case. Since robots can be assigned more than one target, the second stage optimizing for control is shown to take the form of a multi-objective control problem with conflicting objectives, and a strategy is proposed to solve it for real-time applications. A novel information-gain-based tracking objective is developed, which can be used to solve the two stages, suited specifically for the scenario under consideration. Simulation results show that the novel approach, called bundle-based assignment and control (BBAC), optimizing the novel tracking objective, outperforms existing algorithms and achieves performance very close to that of the optimal solution in a shorter time. Physical experiments with a network of ground robots tracking human targets further validate the applicability of these approaches in the real world.

Abstract This paper presents Diver Interest via Pointing in Three Dimensions (DIP-3D), a method to indicate an object of interest from a diver to an autonomous underwater vehicle (AUV) by pointing that includes three-dimensional distance information to discriminate between multiple objects in the AUV’s field of view. Traditional dense stereo vision for distance estimation underwater is challenging because of the relative lack of saliency of scene features and degraded lighting conditions. Yet in many applications, including distance information is necessary for robotic perception of diver pointing when multiple objects appear within the robot’s image view. We subvert the challenges of underwater distance estimation by using sparse reconstruction of specific keypoints in both the left and right images from the robot’s stereo camera to perform pose estimation. Triangulated pose keypoints, along with any object detection method, enable DIP-3D to infer the location of an object of interest when multiple objects are in the AUV’s field of view. By allowing the scuba diver to point at an arbitrary object of interest and enabling the AUV to autonomously decide which object the diver is pointing to, this method permits more natural interaction between AUVs and humans in underwater-human robot collaborative tasks.

Abstract This paper considers a robot moving in a 3D environment that is tasked with estimating a quasi-stationary environmental field (e.g., temperature, concentration of a chemical pollutant, or distribution of light radiation density) in the presence of localization uncertainties, as is typical in underwater or other GPS-denied environments. Gaussian process regression has been widely adopted to model environmental fields. However, a drawback of Gaussian process regression is its difficulty in accounting for data with uncertain input. This work proposes a novel multi-fidelity Gaussian process-based regression approach to address the challenge by splitting the data collected by the robot into different datasets corresponding to the amount of input (localization) uncertainty. Furthermore, a sampling-based trajectory planning algorithm is proposed for adaptive robot exploration that optimizes a field-reconstruction objective function while accommodating resource constraints. The proposed approach is experimentally evaluated using a miniature gliding robotic fish that measures light intensity in a large indoor tank. The adaptive exploration algorithm is tested using both a multi-fidelity Gaussian process model and a baseline single-fidelity model. Two objective functions, based on the information gain and an ergodic metric, respectively, are adopted in the evaluation. The experiments show that, for both objective functions, using multi-fidelity Gaussian process reduces the weighted mean squared error between the model prediction and the ground-truth field compared to using the baseline single-fidelity model that ignores localization uncertainty. Accompanying code available at Coleman (Adaptive exploration under localization uncertainty using multi-fidelity Gaussian processes, 2025, https://github.com/colem404/Adaptive-Exploration-Under-Localization-Uncertainty-Using-Multi-fidelity-Gaussian-Processes/tree/main ).

Abstract Robots should be able to learn complex behaviors from human demonstrations. In practice, these human-provided datasets are inevitably imbalanced : i.e., the human demonstrates some subtasks more frequently than others. State-of-the-art methods default to treating each element of the human’s dataset as equally important. So if—for instance—the majority of the human’s data focuses on reaching a goal, and only a few state-action pairs move to avoid an obstacle, the learning algorithm will place greater emphasis on goal reaching. More generally, misalignment between the relative amounts of data and the importance of that data causes fundamental problems for imitation learning approaches. In this paper we analyze and develop learning methods that automatically account for mixed datasets. We formally prove that imbalanced data leads to imbalanced policies when each state-action pair is weighted equally; these policies emulate the most represented behaviors, and not the human’s complex, multi-task demonstrations. We next explore algorithms that rebalance offline datasets (i.e., reweight the importance of different state-action pairs) without human oversight. Reweighting the dataset can enhance the overall policy performance. However, there is no free lunch: each method for autonomously rebalancing brings its own pros and cons. We formulate these advantages and disadvantages, helping other researchers identify when each type of approach is most appropriate. We conclude by introducing a novel meta-gradient rebalancing algorithm that addresses the primary limitations behind existing approaches. Our experiments show that dataset rebalancing leads to better downstream learning, improving the performance of general imitation learning algorithms without requiring additional data collection. See our project website: https://collab.me.vt.edu/data_curation/ .

Abstract In this paper, we address the problem of coordinating multiple robots to explore large-scale underground areas covered with low-bandwidth communication. Based on the evaluation of existing coordination methods, we found that well-performing methods rely on exchanging significant amounts of data, including maps. Such extensive data exchange becomes infeasible using only low-bandwidth communication, which is suitable for underground environments. Therefore, we propose a coordination method that satisfies low-bandwidth constraints by sharing only the robot’s positions. The proposed method employs a fully decentralized principle called Cross-rank that computes how to distribute robots uniformly at intersections and subsequently orders exploration waypoints based on the traveling salesman problem formulation. The proposed principle has been evaluated based on exploration time, traveled distance, and coverage in five large-scale simulated subterranean environments and a real-world deployment with three quadruped robots. The results suggest that the proposed approach provides a suitable tradeoff between the required communication bandwidth and the time needed for exploration.