Research
Shape-Teacher Alignment Boosts Image-Based Robotic Policies with 3D Shape Knowledge
Researchers propose shape-teacher alignment, a training-time method that transfers 3D shape information from a pretrained Point-MAE model to an image-based policy encoder without changing deployment inputs. This approach improves representation structure, accelerates convergence (up to 5.6x on LIBERO suites), and increases success rates. The method requires no depth input or shape teacher at deployment.
robotic manipulationrepresentation learning3D vision