Tootfinder

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Subhajit Maity, Ayan Kumar Bhunia, Subhadeep Koley, Pinaki Nath Chowdhury, Aneeshan Sain, Yi-Zhe Song
https://arxiv.org/abs/2507.07994

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challenges arise in mastering cross-modal embeddings and handling user-specific sketch styles. Our proposed framework overcomes these hurdles with a prototypical setup, combined with a gr…

CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints
Yuhong Deng, Chao Tang, Cunjun Yu, Linfeng Li, David Hsu
https://arxiv.org/abs/2507.19983 https://

CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints
Clothes manipulation, such as folding or hanging, is a critical capability for home service robots. Despite recent advances, most existing methods remain limited to specific tasks and clothes types, due to the complex, high-dimensional geometry of clothes. This paper presents CLothes mAnipulation with Semantic keyPoints (CLASP), which aims at general-purpose clothes manipulation over different clothes types, T-shirts, shorts, skirts, long dresses, ... , as well as different tasks, folding, flat…

GECO: Geometrically Consistent Embedding with Lightspeed Inference
Regine Hartwig, Dominik Muhle, Riccardo Marin, Daniel Cremers
https://arxiv.org/abs/2508.00746 https://…

GECO: Geometrically Consistent Embedding with Lightspeed Inference
Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a light…

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
Bingran Chen, Baorun Li, Jian Yang, Yong Liu, Guangyao Zhai
https://arxiv.org/abs/2507.14820

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data, suffering from challenges with small objects and sensor noise, or infer 3D information from RGB images, which introduces expensive annotation requirements and discretization issues. Recent methods mitigate some challenges by retaining a 2D representation to estimate grasp keypoints and applying Perspective-n-Point (…

ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
Yunchu Zhang, Shubham Mittal, Zhengyu Zhang, Liyiming Ke, Siddhartha Srinivasa, Abhishek Gupta
https://arxiv.org/abs/2506.13867

ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
Visuomotor policies often suffer from perceptual challenges, where visual differences between training and evaluation environments degrade policy performance. Policies relying on state estimations, like 6D pose, require task-specific tracking and are difficult to scale, while raw sensor-based policies may lack robustness to small visual disturbances.In this work, we leverage 2D keypoints - spatially consistent features in the image frame - as a flexible state representation for robust policy le…