CVPR、ECCV 2026 具身智能优秀论文！
Ranking
Overall
64
Content
70
Popularity
N/A
No observed public metrics; popularity remains neutral/archived.
Merged summary
TL;DR - A curated collection of 150 recent embodied-AI papers from major conferences, spanning vision-language-action models, robotic agents, world models, and reinforcement learning. It highlights emerging approaches for robot perception, planning, and adaptation in dynamic environments.
- Thinker uses robot-specific multimodal datasets, video-plus-final-frame inputs, and two-stage training to improve egocentric temporal and spatial reasoning.
- Thinker reportedly achieves state-of-the-art results on RoboVQA and EgoPlan-Bench2.
- TMoW dynamically routes among world models at test time using multi-granularity environment prototypes.
- TMoW supports zero-shot online prototype updates and few-shot distillation to adapt embodied agents without global retraining.
Sources (1)
CVPR、ECCV 2026 具身智能优秀论文！
Public signals
N/A
TL;DR - A curated collection of 150 recent embodied-AI papers from major conferences, spanning vision-language-action models, robotic agents, world models, and reinforcement learning. It highlights emerging approaches for robot perception, planning, and adaptation in dynamic environments.
- Thinker uses robot-specific multimodal datasets, video-plus-final-frame inputs, and two-stage training to improve egocentric temporal and spatial reasoning.
- Thinker reportedly achieves state-of-the-art results on RoboVQA and EgoPlan-Bench2.
- TMoW dynamically routes among world models at test time using multi-granularity environment prototypes.
- TMoW supports zero-shot online prototype updates and few-shot distillation to adapt embodied agents without global retraining.