Alternatives and similar repositories for v2a-video-model-release

Users that are interested in v2a-video-model-release are comparing it to the libraries listed below

• video-to-action / video-to-action-release
  [ICLR 2025 Spotlight] Grounding Video Models to Actions through Goal Conditioned Exploration
  ☆58Updated 6 months ago
• TEA-Lab / diffusion_reward
  [ECCV 2024] 💐Official implementation of the paper "Diffusion Reward: Learning Rewards via Conditional Video Diffusion"
  ☆111Updated last year
• s-tian / vp2
  VP2 Benchmark (A Control-Centric Benchmark for Video Prediction, ICLR 2023)
  ☆29Updated 8 months ago
• cvlab-columbia / dreamitate
  Dreamitate: Real-World Visuomotor Policy Learning via Video Generation (CoRL 2024)
  ☆55Updated 5 months ago
• michaelyuancb / general_flow
  Repository for "General Flow as Foundation Affordance for Scalable Robot Learning"
  ☆66Updated 10 months ago
• flow-diffusion / AVDC_experiments
  The official codebase for running the experiments described in the AVDC paper.
  ☆18Updated last year
• HeegerGao / FLIP
  Code for FLIP: Flow-Centric Generative Planning for General-Purpose Manipulation Tasks
  ☆79Updated 11 months ago
• homangab / Track-2-Act
  code for the paper Predicting Point Tracks from Internet Videos enables Diverse Zero-Shot Manipulation
  ☆97Updated last year
• bytedance / IRASim
  ☆125Updated 4 months ago
• Streaming-Diffusion-Policy / streaming_diffusion_policy
  Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models
  ☆72Updated 6 months ago
• hxixixh / AdaFlow
  Official PyTorch implementation of AdaFlow
  ☆59Updated last year
• YuejiangLIU / bid_diffusion
  [ICLR25] BID-Robot
  ☆61Updated 3 weeks ago
• yanweiw / itps
  ☆40Updated 7 months ago
• devinluo27 / comp_diffuser_release
  [NeurIPS 2025 Spotlight] Generative Trajectory Stitching through Diffusion Composition
  ☆51Updated 2 months ago
• genaug / genaug
  main augmentation script for real world robot dataset.
  ☆36Updated 2 years ago
• BerkeleyAutomation / mirage
  Mirage: a zero-shot cross-embodiment policy transfer method. Benchmarking code for cross-embodiment policy transfer.
  ☆29Updated last year
• nakamotoo / V-GPS
  official implementation for our paper Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance (CoRL 2024)
  ☆36Updated 6 months ago
• rainbow979 / robodreamer
  ☆84Updated last year
• penn-pal-lab / LIV
  Official repository for "LIV: Language-Image Representations and Rewards for Robotic Control" (ICML 2023)
  ☆126Updated 2 years ago
• luccachiang / robots-pretrain-robots
  [ICLR 2025🎉] This is the official implementation of paper "Robots Pre-Train Robots: Manipulation-Centric Robotic Representation from Lar…
  ☆87Updated 9 months ago
• Kami-code / HandsOnVLM-release
  HandsOnVLM: Vision-Language Models for Hand-Object Interaction Prediction
  ☆40Updated 2 months ago
• PremierTACO / premier-taco
  ☆18Updated last year
• kerrj / eyerobot
  Source code for EyeRobot
  ☆33Updated last month
• t6-thu / awesome-cross-domain-policy-transfer-for-embodied-agents
  [IJCAI'24] An index of algorithms, approaches, and systems on cross-domain policy transfer for embodied agents
  ☆55Updated 9 months ago
• TritiumR / Prompting-with-the-Future
  Implementation of Prompting with the Future: Open-World Model Predictive Control with Interactive Digital Twins. [RSS 2025]
  ☆45Updated 3 weeks ago
• YanjieZe / GNFactor
  [CoRL 2023 Oral] GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
  ☆135Updated last year
• sihengz02 / RoLA
  [CoRL 2025] Robot Learning from Any Images
  ☆33Updated this week
• JonathanYang0127 / omnimimic
  Cross-Embodiment Robot Learning Codebase
  ☆50Updated last year
• Yingdong-Hu / PVM-Robotics
  The repository for a thorough empirical evaluation of pre-trained vision model performance across different downstream policy learning me…
  ☆23Updated 2 years ago
• YanjieZe / rl3d
  [RA-L 2023 & IROS 2023] Visual Reinforcement Learning with Self-Supervised 3D Representations
  ☆82Updated 2 years ago