arXiv AI June 16, 2026 By Wenjing Tang, Xuanjin Jin, Yuan Liu, Renming Huang, Cewu Lu, Panpan Cai

PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty

arXiv:2606. 15654v1 Announce Type: cross Abstract: Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 17

VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents

arXiv:2606. 19729v2 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.

By Marcus Hoerger, Rishikesh Joshi, Rahul Shome, Ian Manchester, Hanna Kurniawati

agents diffusion reinforcement-learning robotics efficiency benchmarks

arXiv AI

Jun 19

VOiLA: Vectorized Online Planning with Learned Diffusion Model for POMDP Agents

arXiv:2606. 19729v1 Announce Type: cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.

By Marcus Hoerger, Rishikesh Joshi, Rahul Shome, Ian Manchester, Hanna Kurniawati

agents diffusion reinforcement-learning robotics efficiency benchmarks

arXiv AI

Jun 10

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

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

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jul 7

Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning

arXiv:2508. 08983v2 Announce Type: replace-cross Abstract: Humans can learn a new manipulation task from one or two demonstrations and then perform it in a new room, with new objects, under new constraints.

By Ben Zandonati, Tom\'as Lozano-P\'erez, Leslie Pack Kaelbling

llms robotics multimodal benchmarks

arXiv AI

Aug 6

Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.

By Houze Xu, Jizhong Li, Ziyi Ye

llms nlp robotics fine-tuning multimodal

arXiv Machine Learning

Jun 16

Geometric Action Model for Robot Policy Learning

arXiv:2606. 17046v1 Announce Type: cross Abstract: Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world.

By Jisang Han, Seonghu Jeon, Jaewoo Jung, Ren\'e Zurbr\"ugg, Honggyu An, Tifanny Portela, Marco Hutter, Marc Pollefeys, Seungryong Kim, Sunghwan Hong

robotics multimodal benchmarks

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VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents

VOiLA: Vectorized Online Planning with Learned Diffusion Model for POMDP Agents

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning

Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

Geometric Action Model for Robot Policy Learning