arXiv Machine Learning

ScaffoldM3C: A Multimodal Sequential Monte Carlo Framework for Generative Stable Construction Planning

ScaffoldM3C is a lightweight, multimodal, auto‑regressive framework that generates stable 3D block constructions by treating the task as a probabilistic next‑block generation problem. It incorporates text, image, and sketch conditioning, introduces a scaffold block token to aid intermediate stability, and uses Sequential Monte Carlo to explore multiple assembly sequences simultaneously. The model is four times smaller than existing baselines, achieving 5‑ to 20‑fold inference speedups while matching or surpassing state‑of‑the‑art construction quality and stability in both simulations and real‑world robot demonstrations.

arXiv AI

Jun 6

Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

arXiv:2606. 05445v1 Announce Type: new Abstract: We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks.

By Jiateng Liu, Bingxuan Li, Zhenhailong Wang, Rushi Wang, Kaiwen Hong, Cheng Qian, Jiayu Liu, Denghui Zhang, Katherine Driggs-Campbell, Manling Li, Heng Ji

llms agents computer-vision multimodal benchmarks

arXiv AI

Jul 7

Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation

arXiv:2607. 05377v1 Announce Type: cross Abstract: While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations.

By Jiaqi Peng, Xiqian Yu, Delin Feng, Yuqiang Yang, Wenzhe Cai, Jing Xiong, Ganlin Yang, Jinliang Zheng, Jiafei Cao, Xueyuan Wei, Jiangmiao Pang, Yuan Shen, Tai Wang

agents robotics fine-tuning multimodal

arXiv AI

Jun 4

UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD

arXiv:2606. 05058v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models.

By Jingyuan Chen, Sheng Jin, Haopeng Sun, Wentao Liu, Chen Qian

llms nlp multimodal benchmarks

arXiv AI

Jul 2

FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model

arXiv:2607. 01212v1 Announce Type: cross Abstract: Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation.

By Chenyang Ma, Yue Yang, Radu Corcodel, Siddarth Jain, Andrew Wu, Chiori Hori, Diego Romeres

robotics multimodal

arXiv Machine Learning

Jun 8

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows

arXiv:2602. 09580v4 Announce Type: replace-cross Abstract: Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly multimodal action distributions.

By Chenyu Yang, Denis Tarasov, Davide Liconti, Romain Guntz, Hehui Zheng, Robert K. Katzschmann

diffusion robotics fine-tuning multimodal

Hugging Face Trending Papers

Jul 15

RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination

Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination.

llms agents robotics multimodal

arXiv AI

Jul 17

RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination

arXiv:2607. 14187v1 Announce Type: new Abstract: Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved.

By Haotian Liang, Mingkang Chen, Yufei Huang, Yuchun Guo, Xiaomeng Zhu, Xiangli Shi, Kaixuan Wang, Yunxuan Mao, Weijie Zhou, Ling Chen, Shirong Zeng, Yueyu Long, Yuchen Si, Yajuan Zhu, Xingyu Zhou, Minghui Wang, Wanjia He, Xin Yang, Lingzhu Xiang, Zhiqing Liu, Bohan Ma, Xiran Huang, Tianshuo Yang, Zhiheng Liu, Xuantang Xiong, Zisheng Lu, Ping Luo, Yao Mu, Han Hu, Zhengyou Zhang

llms agents robotics multimodal

arXiv AI

4d ago

Gondola: Grounded Vision Language Planning for Robotic Manipulation

arXiv:2506.11261v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have shown promising progress in robotic manipulation. However, directly mapping visual observations and...

By Shizhe Chen, Ricardo Garcia, Paul Pacaud, Cordelia Schmid

computer-vision robotics multimodal benchmarks safety

arXiv AI

Jun 17

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

arXiv:2606. 17924v1 Announce Type: cross Abstract: Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation.

By Bochen Yang, Lianlei Shan

llms diffusion reinforcement-learning robotics multimodal benchmarks

arXiv AI

Sep 1

CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies

CF‑VLA introduces a two‑stage coarse‑to‑fine approach for vision‑language‑action policies, replacing multi‑step sampling with a coarse initialization that constructs an action‑aware starting point and a single‑step refinement that corrects residual errors. The coarse stage learns a conditional posterior over endpoint velocity to transform Gaussian noise into a structured initialization, while the fine stage performs a fixed‑time refinement. Experiments on CALVIN and LIBERO demonstrate that CF‑VLA achieves a strong efficiency‑performance trade‑off, reducing action sampling latency by 75.4 % and achieving an 83.0 % real‑robot success rate, outperforming existing NFE=2 methods and matching or surpassing NFE=10 baselines.

By Fan Du, Feng Yan, Jianxiong Wu, Xinrun Xu, Weiye Zhang, Weinong Wang, Yu Guo, Bin Qian, Zhihai He, Fei Wang, Heng Yang

robotics multimodal

Hugging Face Trending Papers

Jun 25

In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics

Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off between semantic fidelity and physical realism. Large language model (LLM)-based approaches can interpret diverse open-vocabulary instructions and compose high-level action plans, but they often generate motions that violate physical constraints.

llms benchmarks safety

Hugging Face Trending Papers

Jul 1

FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model

Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systematic study of real-scale bimanual furniture assembly using Vision-Language-Action models (VLAs).

robotics multimodal

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