Expertise need not monopolize: Action-Specialized Mixture of Experts for
  Vision-Language-Action Learning
Vision-Language-Action (VLA) models are experiencing rapid development and
demonstrating promising capabilities in robotic manipulation tasks. However,
scaling up VLA models presents several critical challenges: (1) Training new
VLA models from scratch demands substantial computational resources and
extensive datasets. Given the current scarcity of robot data, it becomes
particularly valuable to fully leverage well-pretrained VLA model weights
during the scaling process. (2) Real-time control requires carefully balancing
model capacity with computational efficiency. To address these challenges, We
propose AdaMoE, a Mixture-of-Experts (MoE) architecture that inherits
pretrained weights from dense VLA models, and scales up the action expert by
substituting the feedforward layers into sparsely activated MoE layers. AdaMoE
employs a decoupling technique that decouples expert selection from expert
weighting through an independent scale adapter working alongside the
traditional router. This enables experts to be selected based on task relevance
while contributing with independently controlled weights, allowing
collaborative expert utilization rather than winner-takes-all dynamics. Our
approach demonstrates that expertise need not monopolize. Instead, through
collaborative expert utilization, we can achieve superior performance while
maintaining computational efficiency. AdaMoE consistently outperforms the
baseline model across key benchmarks, delivering performance gains of 1.8% on
LIBERO and 9.3% on RoboTwin. Most importantly, a substantial 21.5% improvement
in real-world experiments validates its practical effectiveness for robotic
manipulation tasks.
68f1c62c6e0bef323a68fe9a
Weijie Shen
claimed_verified
2025-10-17T04:34:33.583Z
674d8f71556d9ccbaee9f55a
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/VyZeljrRkqXCpeE1gkbAJ.png
Shen Weijie
user
shenweijie
68f1c62c6e0bef323a68fe9b
Yitian Liu
68f1c62c6e0bef323a68fe9c
Yuhao Wu
68f1c62c6e0bef323a68fe9d
Zhixuan Liang
claimed_verified
2025-10-18T04:49:22.251Z
662a471e94baa018b00c0f5c
/avatars/62a67a2ee6e4b9a7124f8b02b9b3f280.svg
Zhixuan Liang
user
Liang-ZX
68f1c62c6e0bef323a68fe9e
Sijia Gu
68f1c62c6e0bef323a68fe9f
Dehui Wang
68f1c62c6e0bef323a68fea0
Tian Nian
68f1c62c6e0bef323a68fea1
Lei Xu
68f1c62c6e0bef323a68fea2
Yusen Qin
68f1c62c6e0bef323a68fea3
Jiangmiao Pang
68f1c62c6e0bef323a68fea4
Xinping Guan
68f1c62c6e0bef323a68fea5
Xiaokang Yang
68f1c62c6e0bef323a68fea6
Yao Mu
Expertise need not monopolize: Action-Specialized Mixture of Experts for
  Vision-Language-Action Learning
Vision-Language-Action (VLA) models are experiencing rapid development and
demonstrating promising capabilities in robotic manipulation tasks. However,
scaling up VLA models presents several critical challenges: (1) Training new
VLA models from scratch demands substantial computational resources and
extensive datasets. Given the current scarcity of robot data, it becomes
particularly valuable to fully leverage well-pretrained VLA model weights
during the scaling process. (2) Real-time control requires carefully balancing
model capacity with computational efficiency. To address these challenges, We
propose AdaMoE, a Mixture-of-Experts (MoE) architecture that inherits
pretrained weights from dense VLA models, and scales up the action expert by
substituting the feedforward layers into sparsely activated MoE layers. AdaMoE
employs a decoupling technique that decouples expert selection from expert
weighting through an independent scale adapter working alongside the
traditional router. This enables experts to be selected based on task relevance
while contributing with independently controlled weights, allowing
collaborative expert utilization rather than winner-takes-all dynamics. Our
approach demonstrates that expertise need not monopolize. Instead, through
collaborative expert utilization, we can achieve superior performance while
maintaining computational efficiency. AdaMoE consistently outperforms the
baseline model across key benchmarks, delivering performance gains of 1.8% on
LIBERO and 9.3% on RoboTwin. Most importantly, a substantial 21.5% improvement
in real-world experiments validates its practical effectiveness for robotic
manipulation tasks.
AdaMoE, a Mixture-of-Experts architecture, enhances VLA models by leveraging pretrained weights and improving computational efficiency, achieving superior performance in robotic manipulation tasks.
Mixture-of-Experts MoE feedforward layers sparsely activated MoE layers decoupling technique scale adapter router expert selection expert weighting collaborative expert utilization LIBERO RoboTwin