Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand

• URL: http://arxiv.org/abs/2310.20350v1
• Date: Tue, 31 Oct 2023 10:46:19 GMT
• Title: Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand
• Authors: Matthias Humt, Dominik Winkelbauer, Ulrich Hillenbrand and Berthold B\"auml
• Abstract summary: We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module and a grasp predictor. As grasp predictor, we use our two-stage architecture that first generates hand poses using an autoregressive model and then regresses finger joint configurations per pose. Experiments on a physical robot platform demonstrate successful grasping of a wide range of household objects based on a depth image from a single viewpoint.
• Score: 2.4682909476447588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Grasping objects with limited or no prior knowledge about them is a highly relevant skill in assistive robotics. Still, in this general setting, it has remained an open problem, especially when it comes to only partial observability and versatile grasping with multi-fingered hands. We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module that is based on a single depth image, and followed by a grasp predictor that is based on the predicted object shape. The shape completion network is based on VQDIF and predicts spatial occupancy values at arbitrary query points. As grasp predictor, we use our two-stage architecture that first generates hand poses using an autoregressive model and then regresses finger joint configurations per pose. Critical factors turn out to be sufficient data realism and augmentation, as well as special attention to difficult cases during training. Experiments on a physical robot platform demonstrate successful grasping of a wide range of household objects based on a depth image from a single viewpoint. The whole pipeline is fast, taking only about 1 s for completing the object's shape (0.7 s) and generating 1000 grasps (0.3 s).

Related papers

• Cross-Embodiment Dexterous Hand Articulation Generation via Morphology-Aware Learning [82.63833405368159]
  Existing end-to-end methods require training on large-scale datasets for specific hands.<n>We propose an eigengrasp-based, end-to-end framework for cross-embodiment grasp generation.
  arXiv  Detail & Related papers  (2025-10-07T15:57:00Z)
• One Diffusion to Generate Them All [54.82732533013014]
  OneDiffusion is a versatile, large-scale diffusion model that supports bidirectional image synthesis and understanding. It enables conditional generation from inputs such as text, depth, pose, layout, and semantic maps. OneDiffusion allows for multi-view generation, camera pose estimation, and instant personalization using sequential image inputs.
  arXiv  Detail & Related papers  (2024-11-25T12:11:05Z)
• EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild [79.71523320368388]
  Our work aims to reconstruct hand-object interactions from a single-view image. We first design a novel pipeline to estimate the underlying hand pose and object shape. With the initial reconstruction, we employ a prior-guided optimization scheme.
  arXiv  Detail & Related papers  (2024-11-21T16:33:35Z)
• DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image [98.29284902879652]
  We present DICE, the first end-to-end method for Deformation-aware hand-face Interaction reCovEry from a single image. It features disentangling the regression of local deformation fields and global mesh locations into two network branches. It achieves state-of-the-art performance on a standard benchmark and in-the-wild data in terms of accuracy and physical plausibility.
  arXiv  Detail & Related papers  (2024-06-26T00:08:29Z)
• PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction [77.89935657608926]
  We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images. PF-LRM simultaneously estimates the relative camera poses in 1.3 seconds on a single A100 GPU.
  arXiv  Detail & Related papers  (2023-11-20T18:57:55Z)
• Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking [98.91894395941766]
  We propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame. Specifically, we derive this prediction of dynamics through a graph neural network(GNN) that explicitly accounts for both spatial-temporal and visual information. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks.
  arXiv  Detail & Related papers  (2021-06-07T16:36:50Z)
• Multi-Scale Networks for 3D Human Pose Estimation with Inference Stage Optimization [33.02708860641971]
  Estimating 3D human poses from a monocular video is still a challenging task. Many existing methods drop when the target person is cluded by other objects, or the motion is too fast/slow relative to the scale and speed of the training data. We introduce atemporal-temporal network for robust 3D human pose estimation.
  arXiv  Detail & Related papers  (2020-10-13T15:24:28Z)
• 3D Human Pose Estimation using Spatio-Temporal Networks with Explicit Occlusion Training [40.933783830017035]
  Estimating 3D poses from a monocular task is still a challenging task, despite the significant progress that has been made in recent years. We introduce a-temporal video network for robust 3D human pose estimation. We apply multi-scale spatial features for 2D joints or keypoints prediction in each individual frame, and multistride temporal convolutional net-works (TCNs) to estimate 3D joints or keypoints.
  arXiv  Detail & Related papers  (2020-04-07T09:12:12Z)
• Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction [137.28465645405655]
  HANDS'19 is a challenge to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set.
  arXiv  Detail & Related papers  (2020-03-30T19:28:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.