SudeepDasari/visual_foresight

Visual Foresight was a seminal research project (likely originating from Berkeley's BAIR lab circa 2017-2018) that demonstrated how predicting future video frames could be used for robotic planning (MPC). At 2,690 days old, it is an academic artifact rather than a production-ready library. With 144 stars and 33 forks, it shows moderate historical interest but zero current velocity. The defensibility is low because the field has moved from the CNN/RNN-based video prediction models used here to more robust Latent World Models (like DreamerV3) and Diffusion-based trajectory predictors. Frontier labs like Google DeepMind (RT-2) and OpenAI (through robotics partners) are building the modern successors to this paradigm. While the *concept* of visual foresight remains core to robotics, this specific implementation is built on an obsolete stack (TF 1.x) and lacks the performance or generality required by today's standards. It serves primarily as a historical reference for researchers studying the evolution of model-based RL.