Results 21 to 30 of about 365,547 (358)
SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion [PDF]
IEEE International Conference on Robotics and Automation, 2022 Multi-objective optimization problems are ubiquitous in robotics, e.g., the optimization of a robot manipulation task requires a joint consideration of grasp pose configurations, collisions and joint limits. While some demands can be easily hand-designed,
Julen Urain +3 more
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VL-Grasp: a 6-Dof Interactive Grasp Policy for Language-Oriented Objects in Cluttered Indoor Scenes [PDF]
IEEE/RJS International Conference on Intelligent RObots and Systems, 2023 Robotic grasping faces new challenges in human-robot-interaction scenarios. We consider the task that the robot grasps a target object designated by human's language directives.
Yuhao Lu +5 more
semanticscholar +1 more source
Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation [PDF]
IEEE International Conference on Robotics and Automation, 2023 Multi-finger grasping relies on high quality training data, which is hard to obtain: human data is hard to transfer and synthetic data relies on simplifying assumptions that reduce grasp quality.
Dylan Turpin +9 more
semanticscholar +1 more source
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection [PDF]
IEEE International Conference on Computer Vision, 2021 Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt uniform sampling to select grasp candidates.
Chenxi Wang +6 more
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When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection [PDF]
IEEE Robotics and Automation Letters, 2022 In this letter, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well suitable for visual grasping tasks. The first key design is that we adopt
Shaochen Wang, Zhangli Zhou, Z. Kan
semanticscholar +1 more source
The International Journal of Robotics Research, 2022 This paper studies the robustness of grasping in the frictionless plane from a geometric perspective. By treating grasping as a process that shapes the free-space object over time, we define three types of certificates to guarantee success of a grasp: (a) invariance under an initial set, (b) convergence toward a goal grasp, and (c) observability over ...
Bernardo Aceituno-Cabezas +2 more
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Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes [PDF]
IEEE Robotics and Automation Letters, 2023 Fast and robust object grasping in clutter is a crucial component of robotics. Most current works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the guidance information excavated from global semantics, thus limiting high ...
Siang Chen +4 more
semanticscholar +1 more source
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics [PDF]
Robotics: Science and Systems Conference, 2017 To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1.0 in ...
Jeffrey Mahler +7 more
semanticscholar +1 more source
GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF [PDF]
IEEE International Conference on Robotics and Automation, 2022 In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry.
Qiyu Dai +5 more
semanticscholar +1 more source
Is It Pointing to Grasping or Grasping Pointing? [PDF]
Motor Control, 1999 The Smeets and Brenner view on grasping is simple: grasping is in fact pointing. In our comments we examine the model beyond the reach-to-grasp task namely, by grasping (without reaching) of moving objects and eating. The model fits the data of both tasks.
Savelsbergh, G.J.P., van der Kamp, J.
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