InfoFusion Controller: Informed TRRT Star with Mutual Information based on Fusion of Pure Pursuit and MPC for Enhanced Path Planning Seongjun Choi Kyung-Hee University Autonomous Driving Lab, MODULABS, Republic of Korea Y oungbum Kim Korea Aviation University Autonomous Driving Lab, MODULABS, Republic of Korea Nam Woo Kim Unity T echnologies Autonomous Driving Lab, MODULABS, Republic of Korea Mansun Shin SP ACEEDUING Co., Ltd. Autonomous Driving Lab, MODULABS, Republic of Korea Byunggi Chae Auroka Pankyo Autonomous Driving Lab, MODULABS, Republic of Korea Sungjin Lee Dong Seoul University, Autonomous Driving Lab, MODULABS, Republic of Korea Abstract --In this paper, we propose the InfoFusion Controller, an advanced path planning algorithm that integrates both global and local planning strategies to enhance autonomous driving in complex urban environments. The global planner utilizes the informed Theta-Rapidly-exploring Random Tree Star (Informed-TRRT*) algorithm to generate an optimal reference path, while the local planner combines Model Predictive Control (MPC) and Pure Pursuit algorithms. Mutual Information (MI) is employed to fuse the outputs of the MPC and Pure Pursuit controllers, effectively balancing their strengths and compensating for their weaknesses. The proposed method addresses the challenges of navigating in dynamic environments with unpredictable obstacles by reducing uncertainty in local path planning and improving dynamic obstacle avoidance capabilities.

Monte Carlo Tree Search (MCTS) has emerged as a powerful tool for decision-making in robotics, enabling efficient exploration of large search spaces. However, traditional MCTS methods struggle in environments characterized by high uncertainty and noisy data due to their reliance on final-step reward evaluation. The lack of intermediate feedback during search often results in suboptimal decision-making and computational inefficiencies. This paper introduces Reward-Centered ReST-MCTS, a novel framework that enhances MCTS by incorporating intermediate reward shaping. The core of our approach is the Rewarding Center, which refines search trajectories by dynamically assigning partial rewards using rule-based validation, heuristic guidance, and neural estimation. By integrating these mechanisms, our method enables real-time optimization of search paths, mitigating the effects of error propagation. We evaluate Reward-Centered ReST-MCTS in robotic manipulation tasks under high uncertainty, demonstrating consistent improvements in decision accuracy. Compared to baseline methods, including Chain-of-Thought (CoT) prompting and Vanilla ReST-MCTS, our framework achieves a 2-4% accuracy improvement while maintaining computational feasibility. Ablation studies confirm the effectiveness of intermediate feedback in search refinement, particularly in pruning incorrect decision paths early. Furthermore, robustness tests show that our method retains high performance across varying levels of uncertainty.

Imitation learning frameworks for robotic manipulation have drawn attention in the recent development of language model grounded robotics. However, the success of the frameworks largely depends on the coverage of the demonstration cases: When the demonstration set does not include examples of how to act in all possible situations, the action may fail and can result in cascading errors. To solve this problem, we propose a framework that uses serialized Finite State Machine (FSM) to generate demonstrations and improve the success rate in manipulation tasks requiring a long sequence of precise interactions. To validate its effectiveness, we use environmentally evolving and long-horizon puzzles that require long sequential actions. Experimental results show that our approach achieves a success rate of up to 98 in these tasks, compared to the controlled condition using existing approaches, which only had a success rate of up to 60, and, in some tasks, almost failed completely.

We address the problem of object arrangement and scheduling for sequential 3D printing. Unlike the standard 3D printing, where all objects are printed slice by slice at once, in sequential 3D printing, objects are completed one after other. In the sequential case, it is necessary to ensure that the moving parts of the printer do not collide with previously printed objects. We look at the sequential printing problem from the perspective of combinatorial optimization. We propose to express the problem as a linear arithmetic formula, which is then solved using a solver for satisfiability modulo theories (SMT). However, we do not solve the formula expressing the problem of object arrangement and scheduling directly, but we have proposed a technique inspired by counterexample guided abstraction refinement (CEGAR), which turned out to be a key innovation to efficiency.

We present a hierarchical safe auto-taxiing framework to enhance the automated ground operations of multiple unmanned aircraft systems (multi-UAS). The auto-taxiing problem becomes particularly challenging due to (i) unknown disturbances, such as crosswind affecting the aircraft dynamics, (ii) taxiway incursions due to unplanned obstacles, and (iii) spatiotemporal conflicts at the intersections between multiple entry points in the taxiway. To address these issues, we propose a hierarchical framework, i.e., SAFE-TAXI, combining centralized spatiotemporal planning with decentralized MPC-CBF-based control to safely navigate the aircraft through the taxiway while avoiding intersection conflicts and unplanned obstacles (e.g., other aircraft or ground vehicles). Our proposed framework decouples the auto-taxiing problem temporally into conflict resolution and motion planning, respectively. Conflict resolution is handled in a centralized manner by computing conflict-aware reference trajectories for each aircraft. In contrast, safety assurance from unplanned obstacles is handled by an MPC-CBF-based controller implemented in a decentralized manner. We demonstrate the effectiveness of our proposed framework through numerical simulations and experimentally validate it using Night Vapor, a small-scale fixed-wing test platform.

Adapting quickly to dynamic, uncertain environments-often called "open worlds"-remains a major challenge in robotics. Traditional Task and Motion Planning (TAMP) approaches struggle to cope with unforeseen changes, are data-inefficient when adapting, and do not leverage world models during learning. We address this issue with a hybrid planning and learning system that integrates two models: a low level neural network based model that learns stochastic transitions and drives exploration via an Intrinsic Curiosity Module (ICM), and a high level symbolic planning model that captures abstract transitions using operators, enabling the agent to plan in an "imaginary" space and generate reward machines. Our evaluation in a robotic manipulation domain with sequential novelty injections demonstrates that our approach converges faster and outperforms state-of-the-art hybrid methods.

In this paper, we present SeGMan, a hybrid motion planning framework that integrates sampling-based and optimization-based techniques with a guided forward search to address complex, constrained sequential manipulation challenges, such as pick-and-place puzzles. SeGMan incorporates an adaptive subgoal selection method that adjusts the granularity of subgoals, enhancing overall efficiency. Furthermore, proposed generalizable heuristics guide the forward search in a more targeted manner. Extensive evaluations in maze-like tasks populated with numerous objects and obstacles demonstrate that SeGMan is capable of generating not only consistent and computationally efficient manipulation plans but also outperform state-of-the-art approaches.

Taiwan is learning from companies in Ukraine that continue to operate during the country's fight against Russia, a senior Taiwan official said Wednesday, as the island speeds up contingency planning amid heightened Chinese threats. China claims democratically governed Taiwan as its own territory, despite the objection of the government in Taipei, and has ramped up its military pressure against the island in recent years, including holding several rounds of major war games. "We hope to learn from Ukraine's first-hand experiences -- how private companies helped build the resilience of its government and society during wartime," said a senior Taiwan security official, who requested anonymity due to the sensitivity of the matter.

We offer a new in-depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. This problem is central to enhancing ROMIE's operational efficiency and competitiveness against human labor by optimizing cost and time. The primary aim of this research is to advance GPP by developing, evaluating, and improving a cost-efficient software and web application. We delve into an extensive comparison and analysis of Google operations research (OR)-Tools optimization algorithms. Our study is driven by the goal of applying and testing the limits of OR-Tools capabilities by integrating Reinforcement Learning techniques for the first time. This enables us to compare these methods with OR-Tools, assessing their computational effectiveness and real-world application efficiency. Our analysis seeks to provide insights into the effectiveness and practical application of each technique. Our findings indicate that Q-Learning stands out as the optimal strategy, demonstrating superior efficiency by deviating only 1.2% on average from the optimal solutions across our datasets.

Manipulating a deformable linear object (DLO) such as wire, cable, and rope is a common yet challenging task due to their high degrees of freedom and complex deformation behaviors, especially in an environment with obstacles. Existing local control methods are efficient but prone to failure in complex scenarios, while precise global planners are computationally intensive and difficult to deploy. This paper presents an efficient, easy-to-deploy framework for collision-free DLO manipulation using mobile manipulators. We demonstrate the effectiveness of leveraging standard planning tools for high-dimensional DLO manipulation without requiring custom planners or extensive data-driven models. Our approach combines an off-the-shelf global planner with a real-time local controller. The global planner approximates the DLO as a series of rigid links connected by spherical joints, enabling rapid path planning without the need for problem-specific planners or large datasets. The local controller employs control barrier functions (CBFs) to enforce safety constraints, maintain the DLO integrity, prevent overstress, and handle obstacle avoidance. It compensates for modeling inaccuracies by using a state-of-the-art position-based dynamics technique that approximates physical properties like Young's and shear moduli. We validate our framework through extensive simulations and real-world demonstrations. In complex obstacle scenarios-including tent pole transport, corridor navigation, and tasks requiring varied stiffness-our method achieves a 100% success rate over thousands of trials, with significantly reduced planning times compared to state-of-the-art techniques. Real-world experiments include transportation of a tent pole and a rope using mobile manipulators. We share our ROS-based implementation to facilitate adoption in various applications.