Abstract--In mobile robot shared control, effectively understanding human motion intention is critical for seamless human-robot collaboration. This paper presents a novel shared control framework featuring planning-level intention prediction. A path replanning algorithm is designed to adjust the robot's desired trajectory according to inferred human intentions. T o represent future motion intentions, we introduce the concept of an intention domain, which serves as a constraint for path replanning. The intention-domain prediction and path replanning problems are jointly formulated as a Markov Decision Process and solved through deep reinforcement learning. In addition, a V oronoi-based human trajectory generation algorithm is developed, allowing the model to be trained entirely in simulation without human participation or demonstration data. Extensive simulations and real-world user studies demonstrate that the proposed method significantly reduces operator workload and enhances safety, without compromising task efficiency compared with existing assistive teleoperation approaches. OBILE robots have advanced significantly in locomotion, perception, and navigation. However, they still struggle to handle demanding real-world tasks such as search and rescue. Their limitations in perception and cognitive awareness prevent them from adapting to complex and unpredictable environments. A promising direction to overcome these challenges is the integration of a human operator into the system, which is often referred to as a shared control framework. As a result, system performance can be substantially improved. In many tasks, mobile robots are expected to reach a target location or follow a predefined path.

The trajectory of AI development suggests that we will increasingly rely on agent-based systems composed of independently developed agents with different information, privileges, and tools. The success of these systems will critically depend on effective collaboration among these heterogeneous agents, even under partial observability. Despite intense interest, few empirical studies have evaluated such agent-agent collaboration at scale. We propose a collaborative maze-solving benchmark that (i) isolates collaborative capabilities, (ii) modulates problem complexity, (iii) enables scalable automated grading, and (iv) imposes no output-format constraints, preserving ecological plausibility. Using this framework, we evaluate 32 leading open- and closed-source models in solo, homogeneous, and heterogeneous pairings. Our results reveal a "collaboration gap": models that perform well solo often degrade substantially when required to collaborate. Collaboration can break down dramatically; for instance, small distilled models that solve mazes well alone may fail almost completely in certain pairings. We find that starting with the stronger agent often improves outcomes, motivating a "relay inference" approach where the stronger agent leads before handing off to the weaker one, closing much of the gap. Our findings argue for (1) collaboration-aware evaluation, (2) training strategies developed to enhance collaborative capabilities, and (3) interaction design that reliably elicits agents' latent skills, guidance that applies to AI-AI and human-AI collaboration.

We consider the online versions of two fundamental routing problems, traveling salesman (TSP) and dial-a-ride (DARP), which have a variety of relevant applications in logistics and robotics. The online versions of these problems concern with efficiently serving a sequence of requests presented in a real-time on-line fashion located at points of a metric space by servers (salesmen/vehicles/robots). In this paper, motivated from real-world applications, such as Uber/Lyft rides, where some limited knowledge is available on the future requests, we propose the {\em spatial locality} model that provides in advance the distance within which new request(s) will be released from the current position of server(s). We study the usefulness of this advanced information on achieving the improved competitive ratios for both the problems with $k\geq 1$ servers, compared to the competitive results established in the literature without such spatial locality consideration. We show that small locality is indeed useful in obtaining improved competitive ratios irrespective of the metric space.

Recently, there has been growing interest in leveraging large language models (LLMs) to generate symbolic world models from textual descriptions. Although LLMs have been extensively explored in the context of world modeling, prior studies encountered several challenges, including evaluation randomness, dependence on indirect metrics, and a limited domain scope. To address these limitations, we introduce a novel benchmark, Text2World, based on planning domain definition language (PDDL), featuring hundreds of diverse domains and employing multi-criteria, execution-based metrics for a more robust evaluation. We benchmark current LLMs using Text2World and find that reasoning models trained with large-scale reinforcement learning outperform others. However, even the best-performing model still demonstrates limited capabilities in world modeling. Building on these insights, we examine several promising strategies to enhance the world modeling capabilities of LLMs, including test-time scaling, agent training, and more. We hope that Text2World can serve as a crucial resource, laying the groundwork for future research in leveraging LLMs as world models. The project page is available at https://text-to-world.github.io/.

Mobile robot path planning in complex environments remains a significant challenge, especially in achieving efficient, safe and robust paths. The traditional path planning techniques like DRL models typically trained for a given configuration of the starting point and target positions, these models only perform well when these conditions are satisfied. In this paper, we proposed a novel path planning framework that embeds Large Language Models to empower mobile robots with the capability of dynamically interpreting natural language commands and autonomously generating efficient, collision-free navigation paths. The proposed framework uses LLMs to translate high-level user inputs into actionable waypoints while dynamically adjusting paths in response to obstacles. We experimentally evaluated our proposed LLM-based approach across three different environments of progressive complexity, showing the robustness of our approach with llama3.1 model that outperformed other LLM models in path planning time, waypoint generation success rate, and collision avoidance. This underlines the promising contribution of LLMs for enhancing the capability of mobile robots, especially when their operation involves complex decisions in large and complex environments. Our framework has provided safer, more reliable navigation systems and opened a new direction for the future research. The source code of this work is publicly available on GitHub.

Reinforcement Learning (RL) mathematically formulates decision-making with Markov Decision Process (MDP). With MDPs, researchers have achieved remarkable breakthroughs across various domains, including games, robotics, and language models. This paper seeks a new possibility, Natural Language Reinforcement Learning (NLRL), by extending traditional MDP to natural language-based representation space. Specifically, NLRL innovatively redefines RL principles, including task objectives, policy, value function, Bellman equation, and policy iteration, into their language counterparts. With recent advancements in large language models (LLMs), NLRL can be practically implemented to achieve RL-like policy and value improvement by either pure prompting or gradient-based training. Experiments over Maze, Breakthrough, and Tic-Tac-Toe games demonstrate the effectiveness, efficiency, and interpretability of the NLRL framework among diverse use cases. Our code will be released at https://github.com/waterhorse1/Natural-language-RL.

Not yet. We present SPACE, a benchmark that systematically evaluates spatial cognition in frontier models. Our benchmark builds on decades of research in cognitive science. It evaluates large-scale mapping abilities that are brought to bear when an organism traverses physical environments, smaller-scale reasoning about object shapes and layouts, and cognitive infrastructure such as spatial attention and memory. For many tasks, we instantiate parallel presentations via text and images, allowing us to benchmark both large language models and large multimodal models. Results suggest that contemporary frontier models fall short of the spatial intelligence of animals, performing near chance level on a number of classic tests of animal cognition.

Due to their architecture and how they are trained, artificial neural networks are typically not robust toward pruning, replacing, or shuffling layers at test time. However, such properties would be desirable for different applications, such as distributed neural network architectures where the order of execution cannot be guaranteed or parts of the network can fail during inference. In this work, we address these issues through a number of proposed training approaches for vision transformers whose most important component is randomizing the execution order of attention modules at training time. We show that with our proposed approaches, vision transformers are indeed capable to adapt to arbitrary layer execution orders at test time assuming one tolerates a reduction (about 20\%) in accuracy at the same model size. We also find that our trained models can be randomly merged with each other resulting in functional ("Frankenstein") models without loss of performance compared to the source models. Finally, we layer-prune our models at test time and find that their performance declines gracefully.

The L\'evy walk, a type of random walk characterized by linear step lengths that follow a power-law distribution, is observed in the migratory behaviors of various organisms, ranging from bacteria to humans. Notably, L\'evy walks with power exponents close to two are frequently observed, though their underlying causes remain elusive. This study introduces a simplified, abstract random walk model designed to produce inverse square L\'evy walks, also known as Cauchy walks and explores the conditions that facilitate these phenomena. In our model, agents move toward a randomly selected destination in multi-dimensional space, and their movement strategy is parameterized by the extent to which they pursue the shortest path. When the search cost is proportional to the distance traveled, this parameter effectively reflects the emphasis on minimizing search costs. Our findings reveal that strict adherence to this cost minimization constraint results in a Brownian walk pattern. However, removing this constraint transitions the movement to an inverse square L\'evy walk. Therefore, by modulating the prioritization of search costs, our model can seamlessly alternate between Brownian and Cauchy walk dynamics. This model has the potential to be utilized for exploring the parameter space of an optimization problem.

Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation. This is observed in the natural world and has applications in robotics, including natural disaster search and rescue, wild animal tracking, and perimeter surveillance and patrol. Recently, large language models (LLMs) have displayed an impressive ability to solve various collaboration tasks as individual decision-makers. Solving multi-agent flocking with LLMs would demonstrate their usefulness in situations requiring spatial and decentralized decision-making. Yet, when LLM-powered agents are tasked with implementing multi-agent flocking, they fall short of the desired behavior. After extensive testing, we find that agents with LLMs as individual decision-makers typically opt to converge on the average of their initial positions or diverge from each other. After breaking the problem down, we discover that LLMs cannot understand maintaining a shape or keeping a distance in a meaningful way. Solving multi-agent flocking with LLMs would enhance their ability to understand collaborative spatial reasoning and lay a foundation for addressing more complex multi-agent tasks. This paper discusses the challenges LLMs face in multi-agent flocking and suggests areas for future improvement and research.