Autonomous robots are increasingly deployed in dynamic and unstructured environments, where they must plan and execute complex tasks under uncertainty. Classical planning approaches, typically modeled in PDDL and solved with heuristic search, provide a principled foundation for task planning (Edelkamp and Schr odl, 2011; Geffner and Bonet, 2013). However, these methods rely on explicit domain models that enumerate preconditions and effects of actions. In practice, such models often omit implicit commonsense knowledge, for example, that a container must be upright before pouring, or that water must be boiled before making tea. The absence of such knowledge can lead to plans that are logically correct but physically invalid. Cognitive robotics research seeks to bridge symbolic reasoning with robot perception and control (Ghallab et al., 2004). While significant progress has been made in integrating planning with motion control and execution, robots still lack the ability to autonomously infer commonsense constraints that humans consider obvious. Large Language Models (LLMs), trained on massive corpora of human knowledge, present a promising avenue for addressing this gap. LLMs can generate likely preconditions, subgoals, and contextual constraints from natural language task descriptions, potentially enriching classical planning models. 1

Nonholonomic constraints restrict feasible velocities without reducing configuration-space dimension, which makes collision-free geometric paths generally non-executable for car-like robots. Ackermann steering further imposes curvature bounds and forbids in-place rotation, so escaping from narrow dead ends typically requires tightly sequenced forward and reverse maneuvers. Classical planners that decouple global search and local steering struggle in these settings because narrow passages occupy low-measure regions and nonholonomic reachability shrinks the set of valid connections, which degrades sampling efficiency and increases sensitivity to clearances. We study nonholonomic narrow dead-end escape for Ackermann vehicles and contribute three components. First, we construct a generator that samples multi-phase forward-reverse trajectories compatible with Ackermann kinematics and inflates their envelopes to synthesize families of narrow dead ends that are guaranteed to admit at least one feasible escape. Second, we construct a training environment that enforces kinematic constraints and train a policy using the soft actor-critic algorithm. Third, we evaluate against representative classical planners that combine global search with nonholonomic steering. Across parameterized dead-end families, the learned policy solves a larger fraction of instances, reduces maneuver count, and maintains comparable path length and planning time while under the same sensing and control limits. We provide our project as open source at https://github.com/gitagitty/cisDRL-RobotNav.git

The rapid advancement of AI has led to the development of techniques capable of understanding and executing complex tasks. Among these, Large Language Models (LLMs) have emerged as a powerful tool for interpreting natural language, enabling machines to comprehend and respond to human requests with remarkable accuracy [4]. However, the challenge remains in translating these requests into valid (and ideally optimal) plans that can be executed in real-world environments. In particular, we are interested on planning problems that involve the integration of standard office-related tasks, such as handling emails/calendars, managing presentations or databases, connecting to company APIs, or even running machine learning tasks. One of the pioneering efforts in this domain is the development of softbots, as introduced by Etizioni et al. [8].

Robot navigation is increasingly crucial across applications like delivery services and warehouse management. The integration of Reinforcement Learning (RL) with classical planning has given rise to meta-planners that combine the adaptability of RL with the explainable decision-making of classical planners. However, the exploration capabilities of RL-based meta-planners during training are often constrained by the capabilities of the underlying classical planners. This constraint can result in limited exploration, thereby leading to sampling skew issues. To address these issues, our paper introduces a novel framework, DIGIMON, which begins with behavior-guided diagnosis for exploration bottlenecks within the meta-planner and follows up with a mitigation strategy that conducts up-sampling from diagnosed bottleneck data. Our evaluation shows 13.5%+ improvement in navigation performance, greater robustness in out-of-distribution environments, and a 4x boost in training efficiency. DIGIMON is designed as a versatile, plug-and-play solution, allowing seamless integration into various RL-based meta-planners.

Prompting foundation models such as large language models (LLMs) and vision-language models (VLMs) requires extensive domain knowledge and manual efforts, resulting in the so-called "prompt engineering" problem. To improve the performance of foundation models, one can provide examples explicitly [1] or implicitly [2], or encourage intermediate reasoning steps [3, 4]. Despite all the efforts, their performance in long-horizon reasoning tasks is still limited. Classical planning methods, including those defined by Planning Domain Definition Language (PDDL), are strong in ensuring the soundness, completeness and efficiency in planning tasks [5]. However, those classical planners rely on predefined states and actions, and do not perform well in open-world scenarios. We aim to enjoy the openness of VLMs in scene understanding while retaining the strong long-horizon reasoning capabilities of classical planners. Our key idea is to extract domain knowledge from classical planners for prompting VLMs towards enabling classical planners that are visually grounded and responsive to open-world situations. Given the natural connection between planning symbols and human language, this paper investigates how pre-trained VLMs can assist the robot in realizing symbolic plans generated by classical planners, while avoiding the engineering efforts of checking the outcomes of each action.

Classical navigation planners can provide safe navigation, albeit often suboptimally and with hindered human norm compliance. ML-based, contemporary autonomous navigation algorithms can imitate more natural and humancompliant navigation, but usually require large and realistic datasets and do not always provide safety guarantees. We present an approach that leverages a classical algorithm to guide reinforcement learning. This greatly improves the results and convergence rate of the underlying RL algorithm and requires no human-expert demonstrations to jump-start the process. Additionally, we incorporate a practical fallback system that can switch back to a classical planner to ensure safety. The outcome is a sample efficient ML approach for mobile navigation that builds on classical algorithms, improves them to ensure human compliance, and guarantees safety.

Navigation of terrestrial robots is typically addressed either with localization and mapping (SLAM) followed by classical planning on the dynamically created maps, or by machine learning (ML), often through end-to-end training with reinforcement learning (RL) or imitation learning (IL). Recently, modular designs have achieved promising results, and hybrid algorithms that combine ML with classical planning have been proposed. Existing methods implement these combinations with hand-crafted functions, which cannot fully exploit the complementary nature of the policies and the complex regularities between scene structure and planning performance. Our work builds on the hypothesis that the strengths and weaknesses of neural planners and classical planners follow some regularities, which can be learned from training data, in particular from interactions. This is grounded on the assumption that, both, trained planners and the mapping algorithms underlying classical planning are subject to failure cases depending on the semantics of the scene and that this dependence is learnable: for instance, certain areas, objects or scene structures can be reconstructed easier than others. We propose a hierarchical method composed of a high-level planner dynamically switching between a classical and a neural planner. We fully train all neural policies in simulation and evaluate the method in both simulation and real experiments with a LoCoBot robot, showing significant gains in performance, in particular in the real environment. We also qualitatively conjecture on the nature of data regularities exploited by the high-level planner.

The classical method of autonomous racing uses real-time localisation to follow a precalculated optimal trajectory. In contrast, end-to-end deep reinforcement learning (DRL) can train agents to race using only raw LiDAR scans. While classical methods prioritise optimization for high-performance racing, DRL approaches have focused on low-performance contexts with little consideration of the speed profile. This work addresses the problem of using end-to-end DRL agents for high-speed autonomous racing. We present trajectory-aided learning (TAL) that trains DRL agents for high-performance racing by incorporating the optimal trajectory (racing line) into the learning formulation. Our method is evaluated using the TD3 algorithm on four maps in the open-source F1Tenth simulator. The results demonstrate that our method achieves a significantly higher lap completion rate at high speeds compared to the baseline. This is due to TAL training the agent to select a feasible speed profile of slowing down in the corners and roughly tracking the optimal trajectory.