3D-MoE: Towards Spatial Intelligence with Mixture-of-Experts for 3D Reasoning and Action Generation

arXiv:2501.16698 · cs.CL, cs.CV, cs.RO · Submitted 2025-01-28 · Read on arXiv

cs.CL, cs.CV, cs.RO

Submitted: 2025-01-28

Updated: 2026-09-20

Comments: Accepted to EMNLP 2026. 20 pages

Code: https://github.com/hpcaitech/Open-Sora

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 23× in this paper · explained in 2 episodes
• Rectified Flow — 19× in this paper · explained in 3 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Embodied AI — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Mixture of Experts — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Exact Match — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Mixture-of-Experts (MoE) Models — 1× in this paper · explained in 3 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• OpenVLA: An Open-Source Vision-Language-Action Model
• Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
• MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
• Visual Instruction Tuning
• RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
• Mixtral of Experts
• 3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
• Latte: Latent Diffusion Transformer for Video Generation
• LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation
• Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
• LLaMA: Open and Efficient Foundation Language Models

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