CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos

• URL: http://arxiv.org/abs/2601.04061v1
• Date: Wed, 07 Jan 2026 16:26:33 GMT
• Title: CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos
• Authors: Chubin Zhang, Jianan Wang, Zifeng Gao, Yue Su, Tianru Dai, Cai Zhou, Jiwen Lu, Yansong Tang,
• Abstract summary: We propose Contrastive Latent Action Pretraining (CLAP), a framework that aligns the visual latent space from videos with a proprioceptive latent space from robot trajectories.<n>CLAP maps video transitions onto a quantized, physically executable codebook.<n>We introduce a dual-formulation VLA framework offering both CLAP-NTP, an autoregressive model excelling at instruction following and object generalization, and CLAP-RF, a Rectified Flow-based policy designed for high-frequency, precise manipulation.
• Score: 73.51386721543135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalist Vision-Language-Action models are currently hindered by the scarcity of robotic data compared to the abundance of human video demonstrations. Existing Latent Action Models attempt to leverage video data but often suffer from visual entanglement, capturing noise rather than manipulation skills. To address this, we propose Contrastive Latent Action Pretraining (CLAP), a framework that aligns the visual latent space from videos with a proprioceptive latent space from robot trajectories. By employing contrastive learning, CLAP maps video transitions onto a quantized, physically executable codebook. Building on this representation, we introduce a dual-formulation VLA framework offering both CLAP-NTP, an autoregressive model excelling at instruction following and object generalization, and CLAP-RF, a Rectified Flow-based policy designed for high-frequency, precise manipulation. Furthermore, we propose a Knowledge Matching (KM) regularization strategy to mitigate catastrophic forgetting during fine-tuning. Extensive experiments demonstrate that CLAP significantly outperforms strong baselines, enabling the effective transfer of skills from human videos to robotic execution. Project page: https://lin-shan.com/CLAP/.

Related papers

• Learning Skills from Action-Free Videos [20.442392109789772]
  We introduce Skill Abstraction from Optical Flow (SOF), a framework that learns latent skills from action-free videos.<n>SOF enables high-level planning over video-derived skills and allows for easier translation of these skills into actions.<n> Experiments show that our approach consistently improves performance in both multitask and long-horizon settings.
  arXiv  Detail & Related papers  (2025-12-23T05:03:33Z)
• Robotic VLA Benefits from Joint Learning with Motion Image Diffusion [114.60268819583017]
  Vision-Language-Action (VLA) models have achieved remarkable progress in robotic manipulation by mapping multimodal observations and instructions directly to actions.<n>We propose joint learning with motion image diffusion, a novel strategy that enhances VLA models with motion reasoning capabilities.<n> Experiments in both simulation and real-world environments demonstrate that joint learning with motion image diffusion improves the success rate of pi-series VLAs to 97.5%.
  arXiv  Detail & Related papers  (2025-12-19T19:07:53Z)
• mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs [5.109732854501585]
  We introduce mimic-video, a novel Video-Action Model (VAM) that pairs a pretrained Internet-scale video model with a flow matching-based action decoder conditioned on its latent representations.<n>Our approach achieves state-of-the-art performance on simulated and real-world robotic manipulation tasks, improving sample efficiency by 10x and convergence speed by 2x compared to traditional VLA architectures.
  arXiv  Detail & Related papers  (2025-12-17T18:47:31Z)
• Latent Action Pretraining Through World Modeling [1.988007188564225]
  We propose LAWM, a model-agnostic framework to pretrain imitation learning models in a self-supervised way.<n>Our framework is designed to be effective for transferring across tasks, environments, and embodiments.
  arXiv  Detail & Related papers  (2025-09-22T21:19:10Z)
• RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation [39.383510768790295]
  RynnVLA-001 is a vision-language-action(VLA) model built upon large-scale video generative pretraining from human demonstrations.<n>The first stage, Ego-Centric Video Generative Pretraining, trains an Image-to-Video model on 12M ego-centric manipulation videos to predict future frames conditioned on an initial frame and a language instruction.<n>The second stage, Human-Centric Trajectory-Aware Modeling, extends this by jointly predicting future keypoint trajectories, thereby effectively bridging visual frame prediction with action prediction.
  arXiv  Detail & Related papers  (2025-09-18T17:58:02Z)
• Physical Autoregressive Model for Robotic Manipulation without Action Pretraining [65.8971623698511]
  We build upon autoregressive video generation models to propose a Physical Autoregressive Model (PAR)<n>PAR leverages the world knowledge embedded in video pretraining to understand physical dynamics without requiring action pretraining.<n>Experiments on the ManiSkill benchmark show that PAR achieves a 100% success rate on the PushCube task.
  arXiv  Detail & Related papers  (2025-08-13T13:54:51Z)
• Latent Action Pretraining from Videos [156.88613023078778]
  We introduce Latent Action Pretraining for general Action models (LAPA)<n>LAPA is an unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot action labels.<n>We propose a method to learn from internet-scale videos that do not have robot action labels.
  arXiv  Detail & Related papers  (2024-10-15T16:28:09Z)
• Any-point Trajectory Modeling for Policy Learning [64.23861308947852]
  We introduce Any-point Trajectory Modeling (ATM) to predict future trajectories of arbitrary points within a video frame. ATM outperforms strong video pre-training baselines by 80% on average. We show effective transfer learning of manipulation skills from human videos and videos from a different robot morphology.
  arXiv  Detail & Related papers  (2023-12-28T23:34:43Z)
• REST: REtrieve & Self-Train for generative action recognition [54.90704746573636]
  We propose to adapt a pre-trained generative Vision & Language (V&L) Foundation Model for video/action recognition. We show that direct fine-tuning of a generative model to produce action classes suffers from severe overfitting. We introduce REST, a training framework consisting of two key components.
  arXiv  Detail & Related papers  (2022-09-29T17:57:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.