Midea Group

“DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation”

“On the real-world folding benchmark, DSWAM achieves 96.3% average success rate versus DeMaVLA's 92.5%, while reducing completion time from 2'18" to 1'44"”

“BF16 TensorRT reduces warmed end-to-end policy latency from 198.2 ms in PyTorch to 73.8 ms on an NVIDIA GeForce RTX 5090 with CUDA 12.9 and TensorRT 10.16.1”

“most VLA policies are still trained mainly as direct observation-and-language-to-action mappings, so their supervision for how the physical scene evolves under robot intervention is indirect compared with video-based world modeling”