A graspability field learned from synthesized grasps provides a dense reward signal for an RL policy that performs closed-loop non-prehensile manipulation leading to successful grasps.
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
abstract
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 randomized poses on a table. We use the resulting dataset, Dex-Net 2.0, to train a Grasp Quality Convolutional Neural Network (GQ-CNN) model that rapidly predicts the probability of success of grasps from depth images, where grasps are specified as the planar position, angle, and depth of a gripper relative to an RGB-D sensor. Experiments with over 1,000 trials on an ABB YuMi comparing grasp planning methods on singulated objects suggest that a GQ-CNN trained with only synthetic data from Dex-Net 2.0 can be used to plan grasps in 0.8sec with a success rate of 93% on eight known objects with adversarial geometry and is 3x faster than registering point clouds to a precomputed dataset of objects and indexing grasps. The Dex-Net 2.0 grasp planner also has the highest success rate on a dataset of 10 novel rigid objects and achieves 99% precision (one false positive out of 69 grasps classified as robust) on a dataset of 40 novel household objects, some of which are articulated or deformable. Code, datasets, videos, and supplementary material are available at http://berkeleyautomation.github.io/dex-net .
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 8roles
background 1polarities
background 1representative citing papers
GraspGen-X extends diffusion 6-DOF grasping to cross-embodiment via swept-volume gripper encoding, trained on procedural grippers and 2B grasps, claiming best zero-shot generalization to novel grippers in sim and real tests.
HITL-D combines diffusion policies with human input for shared robotic control, reducing required joystick axes and improving speed and workload in manipulation tasks per a 12-participant study.
GraspVLA shows that pretraining a grasping model on a billion synthetic action frames enables zero-shot open-vocabulary performance and sim-to-real transfer.
π_{0.5} is a VLA model that achieves long-horizon dexterous manipulation in entirely new homes through co-training on heterogeneous tasks and multi-source data including web and semantic predictions.
PointACT proposes a 3D-aware dual-system VLA policy using multi-scale point-action interaction with bottleneck window self-attention, achieving 10% higher success rates on RLBench-10Tasks over prior pretrained VLAs.
A literature survey of NeRF and neural field methods from 2020-2025, organized by architecture and application taxonomies with benchmarks and dataset overviews, covering both pre- and post-Gaussian Splatting periods.
Research agenda posing questions on open-ended object perception and grasping for robots that learn categories and affordances gradually from experiences rather than from complete upfront training sets.
citing papers explorer
-
Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field
A graspability field learned from synthesized grasps provides a dense reward signal for an RL policy that performs closed-loop non-prehensile manipulation leading to successful grasps.
-
GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
GraspGen-X extends diffusion 6-DOF grasping to cross-embodiment via swept-volume gripper encoding, trained on procedural grippers and 2B grasps, claiming best zero-shot generalization to novel grippers in sim and real tests.
-
HITL-D: Human In The Loop Diffusion Assisted Shared Control
HITL-D combines diffusion policies with human input for shared robotic control, reducing required joystick axes and improving speed and workload in manipulation tasks per a 12-participant study.
-
PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
PointACT proposes a 3D-aware dual-system VLA policy using multi-scale point-action interaction with bottleneck window self-attention, achieving 10% higher success rates on RLBench-10Tasks over prior pretrained VLAs.