The paper introduces a dataset‑free approach to generate planar graph embeddings that meet strict structural and geometric constraints. By integrating parametric graph grammars with safe reinforcement learning, the authors formulate the task as a constrained Markov decision process and use action projection to keep actions within safe sets. Their method consistently outperforms classical and deep generative baselines on a new benchmark suite for constrained, goal‑directed planar graph generation.
Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications. This deficiency arises from a lack of systematic mechanisms to incorporate constraint information during the generation process.
arXiv:2607. 04854v1 Announce Type: new Abstract: Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications.
The paper presents a reinforcement learning method, HSAC, that builds covering structures without relying on rigid, pre‑planned sequences. It uses graph‑structured state representations and a mixed action space to select blocks and adjust their placement continuously, while an efficient exploration strategy incorporates unilateral edges into graph neural networks. HSAC outperforms the prior hybrid‑PPO approach, shows strong sample efficiency, robustness to hyperparameters, and successfully transfers policies from simulation to a real two‑robot 3D‑printed block construction task.
Recent advances in generative planning have made trajectory inpainting a promising approach to offline goal-conditioned reinforcement learning. However, these methods typically specify the planning ho...
arXiv:2606. 30940v1 Announce Type: cross Abstract: Deep learning methods have vastly expanded the capabilities of motion planning in robotics applications, as learning priors from large-scale data has been shown to be essential in capturing the highly complex behavior required for solving tasks such as manipulation or navigation for autonomous vehicles.
HorizonFlow is a hierarchical planner for offline goal-conditioned reinforcement learning that treats the planning horizon as an output rather than a fixed input. It uses a subgoal route planner and an action-prefix controller, both employing insertion-based generation and flow matching, to jointly generate continuous plan content and its length. The method leverages the partially generated plan to guide token insertion and to steer generation toward shorter plans, achieving superior performance on Maze2D, Multi2D, and OGBench benchmarks.
The paper presents HSAC, a reinforcement learning method that builds covering structures without predefined plans, using graph-structured states and a mixed action space of discrete block selection and continuous placement. It extends soft actor-critic with unilateral edges in graph neural networks to efficiently explore while simulating stability. Experiments show HSAC outperforms hybrid-PPO, remains robust to hyperparameters, and successfully transfers to a real two-robot 3D‑printed arch construction.
DeepPlanner is an end-to-end reinforcement learning framework designed to enhance the planning capabilities of deep research agents. It introduces an entropy-based advantage shaping mechanism that allocates larger updates to high-entropy planning tokens and selectively upweights sample-level advantages during planning-intensive rollouts. Experiments on seven deep research benchmarks show that DeepPlanner improves planning quality and achieves state‑of‑the‑art results with a lower training budget.
arXiv:2607. 14424v1 Announce Type: cross Abstract: In recent years Flow Matching has become a prominent method for generative modeling robot motion generation.
arXiv:2601. 22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical.
arXiv:2610.18677v1 Announce Type: cross Abstract: Online decision-making faces a tension between efficient inference and long-horizon reasoning. Value-based methods rely on short-horizon bootstrappin...
arXiv:2609.39665v1 Announce Type: new Abstract: Embodied agents must determine where to act, anticipate the resulting scene changes, and interpret observed outcomes to guide subsequent actions. This...