MoE-ACT: Scaling Multi-Task Bimanual Manipulation with Sparse Task-Conditioned Mixture-of-Experts Transformers
cs.RO
Submitted: 2026-03-16
Updated: 2026-09-21
Project page: https://j3k7.github.io/MoE-ACT
Terminology
Sources
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
- UniManip: General-Purpose Zero-Shot Robotic Manipulation with Agentic Operational Graph
- DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
- Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation
- RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- Qwen2.5-Coder Technical Report
- MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning
- Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
- Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
- Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
- VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
- What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
- RT-1: Robotics Transformer for Real-World Control at Scale
- MoE-DP: An MoE-Enhanced Diffusion Policy for Robust Long-Horizon Robotic Manipulation with Skill Decomposition and Failure Recovery
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