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About this paper
Shengzhe Zhang, Qi Zhang, Dazhong Shen, Chao Wang, Shaopeng Zhai, Hui Xiong
The authors introduce A³-VLA, which uses interaction cues from robot demonstrations to create candidate target and robot regions, then guides a vision-language-action model’s attention and aligns target features with instructions. SAM2 refines regions offline, avoiding extra perception at inference. On five VLABench suites, mean success rises from 0.47 to 0.64; cues may be noisy and require an interaction cue.
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