Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Haoquan Fang, Jiafei Duan, Donovan Clay +26 authors
MolmoAct2 presents an open-action reasoning model for robotics that improves upon previous systems through specialized vision-language-model backbones, new datasets, open-weight action tokenizers, architectural redesign for continuous-action prediction, and adaptive reasoning for reduced latency.
Tencent Robotics X, HY Vision Team, Xumin Yu +19 authors
HY-Embodied-0.5 is a foundation model family for embodied agents featuring Mixture-of-Transformers architecture and iterative post-training for enhanced visual perception and reasoning capabilities.
Mustafa Shukor, Dana Aubakirova, Francesco Capuano +11 authors
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
Shijie Lian, Bin Yu, Xiaopeng Lin +10 authors
PhysBrain 1.0 leverages human egocentric video to generate physical commonsense supervision for vision-language-action models, achieving state-of-the-art performance in embodied control tasks through capability-preserving adaptation.
Hengyi Xie, Chenfei Yao, Xianjin Wu +7 authors
TurboVLA replaces the LLM-centric vision-language-action pipeline with direct vision-language interaction and a compact decoder to cut inference costs while maintaining high robotic manipulation performance.
Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65 authors
RLDX-1 is a general-purpose robotic policy for dexterous manipulation that integrates heterogeneous modalities through a Multi-Stream Action Transformer architecture, demonstrating superior performance in complex real-world tasks compared to existing vision-language-action models.
Jisang Han, Seonghu Jeon, Jaewoo Jung +7 authors
A geometric action model leverages pretrained geometric foundation models to enable language-conditioned manipulation policies with improved accuracy, robustness, and efficiency in 3D physical environments.
Nikita Kachaev, Mikhail Kolosov, Daniil Zelezetsky +2 authors
Systematic study reveals that naive action fine-tuning degrades visual representations in Vision-Language-Action models, but targeted strategies can mitigate this and improve generalization.
Chanyoung Kim, Minwoo Kim, Minseok Kang +2 authors
Vision-Language-Action models show significant performance drops when handling paraphrased instructions due to surface-level matching rather than semantic understanding, highlighting the need for better linguistic generalization metrics.
Nikita Kachaev, Andrey Moskalenko, Matvey Skripkin +10 authors
Act2Answer protocol evaluates embodied vision-language-action models by having agents answer questions through physical actions, revealing knowledge retention and generalization patterns across different semantic categories.
Haozhe Xie, Beichen Wen, Jiarui Zheng +4 authors
DynamicVLA addresses dynamic object manipulation challenges through a compact vision-language-action model with temporal reasoning and closed-loop adaptation, supported by a new benchmark for dynamic manipulation tasks.
Yalcin Tur, Jalal Naghiyev, Haoquan Fang +4 authors
RD-VLA introduces a recurrent architecture for vision-language-action models that adapts computational depth through latent iterative refinement, achieving constant memory usage and improved task success rates.
Ling Xu, Chuyu Han, Borui Li +6 authors
Embodied.cpp is a portable C++ runtime that enables efficient deployment of vision-language-action and world-action models across heterogeneous edge devices through modular execution layers and optimized inference.
Hongyu Qu, Jianzhe Gao, Xiaobin Hu +6 authors
LaMem-VLA introduces a latent-memory-native framework that integrates historical experience into vision-language-action reasoning through coordinated memory components operating in the same latent space.
Mingjie Pan, Jiyao Zhang, Tianshu Wu +3 authors
A novel object-centric representation bridges the gap between Vision-Language Models' high-level reasoning and the low-level precision needed for robotic manipulation tasks, enabling zero-shot generalization across diverse tasks.
Zijian Zhang, Kaiyuan Zheng, Zhaorun Chen +6 authors
GRAPE improves vision-language-action models' performance by aligning policies via preference modeling, enhancing generalizability and allowing customization of objectives like safety and efficiency.
Anthony Brohan, Noah Brown, Justice Carbajal +51 authors
Vision-language-action models, trained on large-scale vision and language data, are integrated into robotic control to enhance generalization and semantic reasoning.
Taewook Kang, Taeheon Kim, Donghyun Shin +1 authors
Vision-Language-Action models can be efficiently adapted to new environments using a single demonstration through weight vector arithmetic that isolates domain-specific information via subspace alignment.
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