arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.
By Jinrui Liu, Bingyan Nie, Boyu Li, Yaran Chen, Yuze Wang, Shunsen He, Haoran Li
The paper introduces a dual‑process architecture for nonlinear motion planning that blends fast, learning‑based intuition (System‑1) with slow, robust symbolic reasoning (System‑2). A metacognitive controller decides when to use each component, aiming to balance speed, precision, and adaptability. Experiments on diverse benchmark environments show consistent improvements in planning efficiency, accuracy, and generalization, highlighting the benefits of integrating learning with structured reasoning.
By Jiayi Yan, Francesco Fabiano, Alessandro Abate
arXiv:2504. 17901v3 Announce Type: replace-cross Abstract: Task and motion planning (TAMP) is a well-established approach for solving long-horizon robot planning problems.
By Benned Hedegaard, Yichen Wei, Ziyi Yang, Ahmed Jaafar, Stefanie Tellex, George Konidaris, Naman Shah
arXiv:2603.08814v2 Announce Type: replace-cross
Abstract: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; y...
By Piyush Gupta, Sangjae Bae, Jiachen Li, David Isele
Reinforced Planning with Latent World Models (RP1) is a novel method that learns to evaluate imagined outcomes via a critic and to improve multi‑step plans through an optimizer trained offline on world‑model roll‑outs. It is the first approach to fully learn plan improvement and can be attached to any pretrained latent world model. In experiments on visual navigation, arm reaching, and robotic manipulation, RP1 outperforms hand‑designed search algorithms, achieving near‑perfect success while using far fewer roll‑outs and running up to 67× faster than the strongest alternative.
By Armin Sommer, Jannik Schilling
Reinforced Planning with Latent World Models introduces RP1, a neural planner that learns to evaluate imagined outcomes via a critic and improve multi‑step plans through an optimizer trained offline on world‑model roll‑outs. Unlike existing planners that are hand‑designed or only inform policies, RP1 fully learns to refine plans and can be attached to any pretrained latent world model. In experiments on visual navigation, arm reaching, and robotic manipulation, RP1 outperforms hand‑designed search algorithms, achieving near‑perfect success while using 1,000× fewer roll‑outs and up to 67× faster inference.