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
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
arXiv:2604. 16509v2 Announce Type: replace-cross Abstract: Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning.
By Adithya V. Sastry, Bibek Poudel, Weizi Li
arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.
By Lidor Erez, Shahaf S. Shperberg, Ayal Taitler
The paper introduces Graph-Guided Quasimetric Dense Reward (G2QDR), a framework that learns a state connectivity model to predict pairwise connectivity strengths in asymmetric environments. These strengths are converted into scalar auxiliary dense rewards, offering continuous guidance across hierarchical levels. G2QDR can be integrated into any existing Goal-Conditioned Hierarchical Reinforcement Learning architecture and shows empirical performance improvements in sparse reward settings with modest computational cost.
By Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup
GEM-MPC is a reinforcement learning method that blends MPPI planning with policy learning to balance exploration and exploitation in high-dimensional continuous control tasks. It trains a policy to clone the planner while also maintaining a KL-regularized policy that explores around the planner’s suggestions, thereby improving the synergy between planning and learning. The approach introduces Gated Prior Distillation, which selectively updates policies from stored planning distributions only when they offer better targets, reducing the influence of stale data without costly reanalysis. Across continuous-control benchmarks, GEM-MPC outperforms existing planning-based baselines while using lower computational budgets.
By Alvaro Serra-Gomez, Thomas Moerland