Learning Generalizable 3D Manipulation With 10 Demonstrations

Learning robust and generalizable manipulation skills from few demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics. Although recent imitation learning methods have achieved impressive results, they often require a large amount of demonstration data and struggle to generalize across different spatial variants. In this work, we propose a framework that learns 3D manipulation policies from only 10 demonstrations while achieving robust generalization to unseen spatial configurations through semantic-guided perception and spatial-equivariant policy learning. Our framework consists of two key modules: a Semantic Guided Perception module that extracts task-aware 3D representations from RGB-D inputs using semantic priors and a Spatial Generalized Decision module implementing a diffusion-based policy that preserves spatial equivariance through denoising. Central to our framework is a spatially equivariant training strategy, which adapts 2D data augmentation principles to 3D manipulation by maintaining gripper-object spatial relationships during trajectory augmentation. We validate our framework through extensive experiments on both simulation benchmarks and real-world robotic systems. Our method demonstrates a significant improvement in success rates over state-of-the-art approaches on a series of challenging tasks, particularly under significant object pose variations. This work shows significant potential to advance efficient and generalizable manipulation skill learning in real-world applications.

Authors

• Yu Ren
• Yang Cong
• Bohao Huang
• Jiahao Long
• Ronghan Chen
• Hongbo Li
• Huijie Fan

Keywords

• Training
• Three-dimensional displays
• Service robots
• Imitation learning
• Semantics
• Noise reduction
• Benchmark testing
• Data augmentation
• Trajectory
• Intelligent robots
• 3D Manipulation
• Spatial Variation
• Equivalency
• Real-world Systems
• 3D Representation
• Policy Learning
• Object Pose
• Improve Success Rates
• Benchmark Simulation
• Robotics Challenge
• Training Data
• Task Performance
• Reference Frame
• Simulation Experiments
• Point Cloud
• Bounding Box
• Diffusion Model
• RGB-D Images
• Manipulation Tasks
• 3D Rotation
• Rotation Vector
• Foundation Model
• Cartesian Position
• 3D Translation
• Background Points
• Real-world Experiments
• Robotic Arm