Learning Expressive and Compositional Motion Representation via Spectral Skills

arXiv:2609.37677 · cs.RO, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.RO, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Steering — 39× in this paper · explained in 2 episodes
• Ablation — 18× in this paper · explained in 1 episode
• Reinforcement Learning — 13× in this paper · explained in 4 episodes
• Actor — 10× in this paper · explained in 3 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 6× in this paper
• APEX — 5× in this paper · explained in 1 episode
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Latent Representation — 4× in this paper · explained in 2 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Error Decomposition — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• RT-H: Action Hierarchies Using Language
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• Object-Centric Dexterous Manipulation from Human Motion Data
• GMT: General Motion Tracking for Humanoid Whole-Body Control
• NaVILA: Legged Robot Vision-Language-Action Model for Navigation
• Spectral Representation-based Reinforcement Learning
• OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
• UniAct: Unified Motion Generation and Action Streaming for Humanoid Robots
• Deep Successor Reinforcement Learning
• CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
• Neural probabilistic motor primitives for humanoid control
• Spectral Decomposition Representation for Reinforcement Learning
• The Riemannian Geometry of Deep Generative Models
• Heracles: Bridging Precise Tracking and Generative Synthesis for General Humanoid Control
• OmniXtreme: Breaking the Generality Barrier in High-Dynamic Humanoid Control
• TextOp: Real-time Interactive Text-Driven Humanoid Robot Motion Generation and Control
• LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction
• Scaling Behavior Foundation Model for Humanoid Robots
• Track Any Motions under Any Disturbances

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