Reproduce “Pick-and-place Manipulation Across Grippers Without Retraining: A Learning-optimization Diffusion Policy Approach” on OpenResearch
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About this paper
Xiangtong Yao, Yirui Zhou, Yuan Meng, Liangyu Dong, Lin Hong, Zitao Zhang, Zhenshan Bing, Kai Huang and 2 more
A learning-optimization hybrid framework enables robotic pick-and-place policies to adapt to diverse gripper configurations without requiring retraining. This Gripper-Aware Diffusion Policy (GADP) achieved an average task success rate of 93.3% for seen objects and 70.0% for unseen objects across six novel grippers, significantly outperforming baselines, while preventing collisions.
Key insights
- Stable, gripper-agnostic grasping knowledge, effectively captured by a processed grasping probability map, is crucial for maintaining consistent approach trajectories despite variations in gripper morphology and viewpoint.
- Explicitly modeling gripper physical characteristics and mapping them to a reference frame allows a single learned policy to generalize across diverse end-effectors without explicit retraining.
- Integrating online, hard safety constraints through an iterative projection method within the diffusion denoising process ensures task completion and collision avoidance, enhancing the robustness of data-driven policies to hardware changes.
Summary by alphaXiv.
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