UMI-Bridge: Action-Anchored Latent Alignment across Human and Robot Manipulation Data

arXiv:2609.18232 · cs.RO · Submitted 2026-09-16 · Read on arXiv

cs.RO

Submitted: 2026-09-16

Updated: 2026-09-28

Terminology

• Alignment — 26× in this paper · explained in 1 episode
• Grounding — 11× in this paper · explained in 3 episodes
• Regularization — 9× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Universal Manipulation Interface — 3× in this paper
• Computer Vision and Pattern Recognition — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Action Decoder — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Two-Stage Framework — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• OpenVLA: An Open-Source Vision-Language-Action Model
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
• EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video
• UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
• MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction
• Causally Debiased Latent Action Model for Embodied Action-Conditioned World Models
• HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Learning to Act without Actions
• Genie: Generative Interactive Environments
• LARA: Latent Action Representation Alignment for Vision-Language-Action Models
• WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos
• EgoMimic: Scaling Imitation Learning via Egocentric Video
• Translation as a Bridging Action: Transferring Manipulation Skills from Humans to Robots
• ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
• EgoGuide: Egocentric Guidance for Efficient Robot-Free Demonstration Collection and Learning
• Bridging Handheld and Teleoperated Supervision for Contact-Rich Manipulation via State-Gated Experts

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving