๐ค AI Summary
ๆฌๆๆๅบME-VLMๆจกๅ๏ผ้่ฟ็ปๅ็ฉ็ๆ็ฅใๆถ็ฉบๆจ็ๅๅคๆจกๆไปฃ็่ฝๅ๏ผ่งฃๅณไบ็ฉ็AIไธญ่ง่ง่ฏญ่จ็่งฃไธ็ฏๅข็บฆๆๆง่กๅ้ฆ็่ๅ้ฎ้ขใ
๐ Abstract
Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and execution feedback. We introduce MachEmbodied-VLM (ME-VLM), a unified vision-language model with two variants, 4B and 35B-A3B, that brings together embodied cognition and multimodal agent capabilities. Our work emphasizes physical perception and spatiotemporal reasoning, together with planning, interaction, and outcome assessment in both digital and physical environments. We construct training data spanning embodied and multimodal agent tasks, including execution observations and feedback to support outcome assessment and decision refinement. The training pipeline comprises embodied capability injection, separate reinforcement learning of embodied and multimodal-agent experts, and multi-teacher on-policy distillation that consolidates their complementary capabilities into a single model. Experiments show competitive performance on both embodied and agent benchmarks, as well as on autonomous-driving and embodied-navigation tasks. For edge deployment, visual token compression, W4A8 quantization, and hardware--software co-optimization enable on-device inference of the 4B variant on the M100, reducing prefill latency from 400 ms to 188 ms.
Project Page: https://machembodied.com/ME-Brain/ME-VLM.html Code Repository: https://github.com/MachEmbodied/ME-VLM