5 papers
Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model
Tao Lin, Yuxin Du, Jiting Liu +14
Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action generation. However, they often s…
PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
Yuanzhe Liu, Jingyuan Zhu, Yuchen Mo +9
Recent advancements in vision-language-action (VLA) models have shown promise in robotic manipulation, yet they continue to struggle with long-horizon, multi-step tasks. Existing m…
Evo-1: Lightweight Vision-Language-Action Model with Preserved Semantic Alignment
Tao Lin, Yilei Zhong, Yuxin Du +11
Vision-Language-Action (VLA) models have emerged as a powerful framework that unifies perception, language, and control, enabling robots to perform diverse tasks through multimodal…
Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding
Tao Lin, Gen Li, Yilei Zhong +5
Vision-Language-Action (VLA) models have emerged as a promising framework for enabling generalist robots capable of perceiving, reasoning, and acting in the real world. These model…
Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts
Yizhou Huang, Fan Yang, Guoliang Zhu +6
Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions.…