arXiv AI August 7, 2026 By Saugat Adhikari, Ashok Prasad Neupane, Pramish Paudel, Ajad Chhatkuli, Danda Pani Paudel

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

arXiv:2608. 06161v1 Announce Type: new Abstract: Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably satisfying task-critical functional constraints.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Sep 22

Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds

arXiv:2603.18532v3 Announce Type: replace-cross Abstract: The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar approaches for f...

By Andrew Choi, Xinjie Wang, Zhizhong Su, Wei Xu

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 2

SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes

arXiv:2602. 09153v2 Announce Type: replace-cross Abstract: Simulation has become a key tool for training and evaluating home robots at scale, yet existing environments fail to capture the diversity and physical complexity of real indoor spaces.

By Nicholas Pfaff, Thomas Cohn, Sergey Zakharov, Rick Cory, Russ Tedrake

agents robotics multimodal

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

3d ago

ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning

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...

By Chenyangguang Zhang, Malgorzata Gwiazda, Guanlong Jiao, Yuanchen Ju, Federico Tombari, Koushil Sreenath, Marc Pollefeys, Sunghwan Hong

llms agents reinforcement-learning robotics fine-tuning multimodal

arXiv AI

Jun 15

3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding

arXiv:2603. 04976v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored.

By Xiongkun Linghu, Jiangyong Huang, Baoxiong Jia, Siyuan Huang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 6

HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes

arXiv:2606. 06390v1 Announce Type: cross Abstract: Indoor scene generation is crucial for robot simulation and modern interior design.

By Wenbo Li, Xiaoliang Ju, Zipeng Qin, Rongyao Fang, Hongsheng Li

llms diffusion robotics multimodal

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Related stories

Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds

SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning

3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding

HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes