GDLAM: Group-Disentangled Latent Action Model for Highly Disentangled Embodied Pretraining

arXiv:2609.26118 · cs.RO · Submitted 2026-08-10 · Read on arXiv

cs.RO

Submitted: 2026-08-10

Updated: 2026-08-10

Terminology

• World Model — 30× in this paper · explained in 7 episodes
• Sparsity — 19× in this paper · explained in 4 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Modularity — 10× in this paper · explained in 9 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Generalization — 9× in this paper · explained in 13 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Linear probe — 6× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Latent Representation — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Group-Disentangled Latent Action Model — 4× in this paper
• Representation Learning — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Anchor Loss — 2× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Pseudo-labels — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Groups — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Action-Conditioned Dynamics — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 4 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Mutual Information Gap — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes

Sources

• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
• Understanding disentangling in $\beta$-VAE
• WorldVLA: Towards Autoregressive Action World Model
• Vidarc: Embodied Video Diffusion Model for Closed-loop Control
• Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control
• DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
• EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video
• OpenVLA: An Open-Source Vision-Language-Action Model
• Evaluating Real-World Robot Manipulation Policies in Simulation
• Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
• DINOv2: Learning Robust Visual Features without Supervision
• WorldArena: A Unified Benchmark for Evaluating Perception and Functional Utility of Embodied World Models
• GigaWorld-0: World Models as Data Engine to Empower Embodied AI
• MIND-V: Hierarchical World Model for Long-Horizon Robotic Manipulation with RL-based Physical Alignment
• JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy

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