CoRL 2026 Poster 🌟 Why can adding more first-person human video actually make a robot worse at a task? Georgia Tech researchers found a problem with traditional behavior cloning. Humans and robots see the world differently, have different bodies, and move in different ways. When human videos are added to training, models can end up learning human motions that robots simply can't perform. The team introduced EgoWAM to address this. Instead of only predicting actions, it also trains robots to predict how the world changes after an action. The team compared three ways to represent those changes: • Pixel: Predicts future frames at the pixel level. • DINO: Predicts semantic features extracted by a vision model, focusing on objects and scene content. • 3D Motion Flow: Predicts how objects move in 3D space, separating camera motion from actual changes in the environment. The team tested EgoWAM on three real-world bimanual tasks: placing a cup on a saucer, bagging groceries, and folding clothes. The experiments involved 1,800 real-robot trials. The results: Pixel prediction offered limited gains. DINO delivered the strongest OOD generalization. Compared with robot-only training, it improved success rates by: • 43 percentage points on cup placement. • 40 points on grocery bagging. • 70 points on clothes folding. Clothes-folding success in OOD tests reached 85%. 3D Flow was better for precise spatial manipulation. It improved in-domain success rates by: • 20 percentage points on cup placement. • 30 points on grocery bagging. Both tasks reached 90% success. On grocery bagging, 3D Flow also delivered a 45-point gain in OOD testing. EgoWAM points to a different way of training robots. Authors: Baoyu Li, Xinchen Yin, Mengying Lin, Yixin Zhang, and Danfei Xu.