VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning
This addresses robustness and spatial reasoning issues in robotic manipulation, offering substantial performance gains in simulation and real-world generalization, though it is incremental in improving existing imitation learning methods.
The paper tackles the 2D-3D mismatch in visual imitation learning for robotic manipulation by introducing VolumeDP, a policy architecture that uses volumetric representation for spatial reasoning, achieving a state-of-the-art 88.8% success rate on the LIBERO benchmark with a 14.8% improvement over baselines.
Imitation learning is a prominent paradigm for robotic manipulation. However, existing visual imitation methods map 2D image observations directly to 3D action outputs, imposing a 2D-3D mismatch that hinders spatial reasoning and degrades robustness. We present VolumeDP, a policy architecture that restores spatial alignment by explicitly reasoning in 3D. VolumeDP first lifts image features into a Volumetric Representation via cross-attention. It then selects task-relevant voxels with a learnable module and converts them into a compact set of spatial tokens, markedly reducing computation while preserving action-critical geometry. Finally, a multi-token decoder conditions on the entire token set to predict actions, thereby avoiding lossy aggregation that collapses multiple spatial tokens into a single descriptor. VolumeDP achieves a state-of-the-art average success rate of 88.8% on the LIBERO simulation benchmark, outperforming the strongest baseline by a substantial 14.8% improvement. It also delivers large performance gains over prior methods on the ManiSkill and LIBERO-Plus benchmarks. Real-world experiments further demonstrate higher success rates and robust generalization to novel spatial layouts, camera viewpoints, and environment backgrounds. Code will be released.