Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects
@inproceedings{Ichnowski2021DexNeRFUA, title={Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects}, author={Jeffrey Ichnowski and Yahav Avigal and Justin Kerr and Ken Goldberg}, booktitle={Conference on Robot Learning}, year={2021} }
The ability to grasp and manipulate transparent objects is a major challenge for robots. Existing depth cameras have difficulty detecting, localizing, and inferring the geometry of such objects. We propose using neural radiance fields (NeRF) to detect, localize, and infer the geometry of transparent objects with sufficient accuracy to find and grasp them securely. We leverage NeRF’s viewindependent learned density, place lights to increase specular reflections, and perform a transparency-aware…
31 Citations
GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF
- Computer ScienceArXiv
- 2022
This work proposes a multiview RGB-based 6-DoF grasp detection network, GraspNeRF, that leverages the generalizable neural radiance (NeRF) to achieve material-agnostic object grasping in clutter and demonstrates that it outperforms all the baselines in all the experiments while remaining in real-time.
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and A Grasping Baseline
- Computer ScienceIEEE Robotics and Automation Letters
- 2022
This work contributes a large-scale real-world dataset for transparent object depth completion, which contains 57,715 RGB-D images from 130 different scenes and proposes an end-to-end depth completion network, which takes the RGB image and the inaccurate depth map as inputs and outputs a refined depth map.
A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulation
- Computer ScienceIEEE Robotics and Automation Letters
- 2022
Extensive experiments show that the proposed methods can predict accurate affordance maps, and significantly improve the depth reconstruction of transparent objects compared to the state-of-the-art method, with the Root Mean Squared Error in meters significantly decreased.
Neural Fields for Robotic Object Manipulation from a Single Image
- Computer ScienceArXiv
- 2022
This work believes this to be the first work to retrieve grasping poses directly from a NeRF-based representation using a single viewpoint (RGB-only), rather than going through a secondary network and/or representation.
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields
- Computer ScienceArXiv
- 2022
It is demonstrated that this system can be used to learn vision-based whole body navigation and ball pushing policies for a 20 degrees of freedom humanoid robot with an actuated head-mounted RGB camera, and to transfer these policies to a real robot.
TransNet: Category-Level Transparent Object Pose Estimation
- Computer ScienceArXiv
- 2022
A two-stage pipeline that learns to estimate category-level transparent object pose using localized depth completion and surface normal estimation, and demonstrates that TransNet achieves improved pose estimation accuracy on transparent objects and key findings from the included ablation studies suggest future directions for performance improvements.
NeRF-Loc: Transformer-Based Object Localization Within Neural Radiance Fields
- Computer ScienceArXiv
- 2022
This work proposes a transformer- based framework NeRF-Loc to extract 3D bounding boxes of objects in NeRF scenes and designs a pair of paralleled transformer encoder branches to encode both the context and details of target objects.
Implicit Object Mapping With Noisy Data
- Computer ScienceArXiv
- 2022
This paper uses the outputs of an object-based SLAM system to bound objects in the scene with coarse primitives and – in concert with instance masks – identify obstructions in the training images to show that object- based NeRFs are robust to pose variations but sensitive to the quality of the instance masks.
AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains
- Computer ScienceArXiv
- 2022
A new methodology for grasp perception to enable robots to grasp as robustly as humans, and develops a dense supervision strategy with real perception and analytic labels in the spatial-temporal domain.
Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
- Computer ScienceArXiv
- 2022
The proposed framework synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects.
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