CoPa introduces a novel framework for robotic manipulation using foundation models. It decomposes the process into task-oriented grasping and task-aware motion planning, showcasing a fine-grained physical understanding of scenes. The framework seamlessly integrates with high-level planning methods for complex tasks.
The content discusses the challenges in low-level robotic control and the importance of common sense knowledge for generalizability. CoPa's innovative design allows it to handle open-set instructions and objects effectively. Real-world experiments demonstrate CoPa's success in completing everyday manipulation tasks with a high rate of success.
Key components like coarse-to-fine grounding and constraint generation are crucial for CoPa's performance. Ablation studies highlight the significance of foundation models, coarse-to-fine design, and constraint generation in achieving successful outcomes. Integration with high-level planning methods showcases CoPa's potential for accomplishing long-horizon tasks.
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