Jimmy Chiun

In multi-robot exploration, a team of mobile robot is tasked with efficiently mapping an unknown environments. While most exploration planners assume omnidirectional sensors like LiDAR, this is impractical for small robots such as drones, where lightweight, directional sensors like cameras may be the only option due to payload constraints. These sensors have a constrained field-of-view (FoV), which adds complexity to the exploration problem, requiring not only optimal robot positioning but also sensor orientation during movement. In this work, we propose MARVEL, a neural framework that leverages graph attention networks, together with novel frontiers and orientation features fusion technique, to develop a collaborative, decentralized policy using multi-agent reinforcement learning (MARL) for robots with constrained FoV. To handle the large action space of viewpoints planning, we further introduce a novel information-driven action pruning strategy. MARVEL improves multi-robot coordination and decision-making in challenging large-scale indoor environments, while adapting to various team sizes and sensor configurations (i.e., FoV and sensor range) without additional training. Our extensive evaluation shows that MARVEL's learned policies exhibit effective coordinated behaviors, outperforming state-of-the-art exploration planners across multiple metrics. We experimentally demonstrate MARVEL's generalizability in large-scale environments, of up to 90 m by 90 m, and validate its practical applicability through successful deployment on a team of real drone hardware.

To perform outdoor visual navigation and search, a robot may leverage satellite imagery to generate visual priors. This can help inform high-level search strategies, even when such images lack sufficient resolution for target recognition. However, many existing informative path planning or search-based approaches either assume no prior information, or use priors without accounting for how they were obtained. Recent work instead utilizes large Vision Language Models (VLMs) for generalizable priors, but their outputs can be inaccurate due to hallucination, leading to inefficient search. To address these challenges, we introduce Search-TTA, a multimodal test-time adaptation framework with a flexible plug-and-play interface compatible with various input modalities (e.g., image, text, sound) and planning methods (e.g., RL-based). First, we pretrain a satellite image encoder to align with CLIP's visual encoder to output probability distributions of target presence used for visual search. Second, our TTA framework dynamically refines CLIP's predictions during search using uncertainty-weighted gradient updates inspired by Spatial Poisson Point Processes. To train and evaluate Search-TTA, we curate AVS-Bench, a visual search dataset based on internet-scale ecological data containing 380k images and taxonomy data. We find that Search-TTA improves planner performance by up to 30.0%, particularly in cases with poor initial CLIP predictions due to domain mismatch and limited training data. It also performs comparably with significantly larger VLMs, and achieves zero-shot generalization via emergent alignment to unseen modalities. Finally, we deploy Search-TTA on a real UAV via hardware-in-the-loop testing, by simulating its operation within a large-scale simulation that provides onboard sensing.

Many multi-robot applications require allocating a team of heterogeneous agents to complete a given set of spatially distributed tasks as quickly as possible, such as search and rescue, area inspection/monitoring, and space exploration. We focus on tasks which can only be initiated when all required agents have arrived, such as detection involving sensor fusion or cooperative assembly requiring robots with different tools/skillsets. Robots dynamically form and disband teams based on diverse abilities needed for each task, which, however, can potentially result in extra idle (waiting) times at the task location. Robots (agents) should take into account the schedules of others to maximize their collective efficiency and reduce overall task completion time (makespan). Conventional methods such as mix-integer programming generally require centralized scheduling and long optimization time, which limits their potential for real-world applications. In this work, we propose a decentralized method that fully relies on reinforcement learning (RL) to train a generalized policy applicable to heterogeneous agents. To address the challenge of complex cooperation learning, we further introduce a constrained flashforward mechanism to guide/constrain the agents' exploration and help them make better predictions. Through an attention mechanism that reasons about both short-term cooperation and long-term scheduling dependency, agents learn to reactively choose their next tasks (and subsequent coalitions) to avoid wasting abilities and to shorten the makespan. We compare our method with state-of-the-art heuristic and mixed-integer programming methods, demonstrating its generalization ability and showing it closely matches or outperforms these baselines while remaining at least two orders of magnitude faster.