arXiv:2602.23934v2 Announce Type: replace-cross
Abstract: This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectu...
By Jingwen Wang, Johannes Kirschner, Paul Rolland, Luis Salamanca, Stefana Parascho
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.
By Gabriel Vallat, Maryam Kamgarpour, Stefana Parascho
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.
The paper introduces PRM4RL, a framework that combines task planning and dense reward generation using large language models for reinforcement learning. It decomposes complex tasks into manageable subtasks and employs a fine‑grained progress function to monitor execution, which is then used to construct a Progress Reward Model for potential‑based reward shaping. Experiments on robotics control tasks show that PRM4RL outperforms existing LLM‑based planning and reward methods, achieving state‑of‑the‑art performance.
By Xiuhui Zhang, Ning Gao, Xingyu Jiang, Yihui Chen, Yuheng Pan, Mohan Zhang, Yue Deng
The paper reviews hierarchical reinforcement learning (HRL) as a method for enabling agents to explore, plan, and learn in complex, open-ended environments by uncovering temporal structure in experience streams. It discusses the unclear definition of what makes a structure useful, the benefits of HRL for decision‑making challenges, and its impact on AI agent performance trade‑offs. The authors survey various HRL methods—from online learning to offline datasets and large language model integration—and outline the challenges and suitable domains for temporal structure discovery.
By Martin Klissarov, Akhil Bagaria, Ziyan Luo, George Konidaris, Doina Precup, Marlos C. Machado
arXiv:2608. 03502v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents.
By Christophe D. Hounwanou, John Emeka Eze, Ya\'e Ulrich Gaba