Task-Critical Part Alignment Improves Imitation of Object Interactions
Task-Critical Part Alignment Improves Imitation of Object Interactions
This paper presents enhancements to the DINOBot framework for imitation learning of object-object interactions. The authors integrate RANSAC for outlier rejection and refine semantic correspondence to focus on task-relevant regions. Evaluated on real-world tasks including picking, insertion, shaking, and pouring, the approach demonstrates a 13.8% improvement in task success rates compared to the baseline.
The authors report a 13.8% improvement in task success rates for object-object interaction imitation using RANSAC-based outlier rejection and refined semantic correspondence within the DINOBot framework, evaluated on real-world manipulation tasks.
Source: arXiv Robotics — research abstracts · Read original article ↗
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mpact of noisy depth data and spurious feature matches. This improves robustness when transferring demonstrations to real-world applications. Second, we refine the semantic correspondence process by focusing on task-relevant interaction regions, ensuring that the learned representation prioritizes critical object features that influence successful manipulation. We evaluated our approach in a range of real-world manip
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ulation tasks, including picking, insertion, shaking, and pouring, using a robotic system. The experimental results demonstrate an overall improvement of 13.8% in task success rates compared to the baseline, along with faster convergence in the alignment process. These results highlight the effectiveness of our enhancements in improving precision and efficiency for object-object interaction imitation in robotics.
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org