6-DOF Grasping for Target-driven Object Manipulation in Clutter

By Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Chris Paxton, Dieter Fox. 2019

238 citations recorded by the source corpus.

Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulator. Most existing data-driven approaches avoid this problem by limiting themselves to top-down planar grasps which is insufficient for many real-world scenarios and greatly limits possible grasps. We present a method that plans 6-DOF grasps for any desired object in a cluttered scene from partial point cloud observations. Our method achieves a grasp success of 80.3%, outperforming baseline approaches by 17.6% and clearing 9 cluttered table scenes (which contain 23 unknown objects and 51 picks in total) on a real robotic platform. By using our learned collision checking module, we can even reason about effective grasp sequences to retrieve objects that are not immediately accessible. Supplementary video can be found here.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction, Robotics & Control

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Citations in the corpus

Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, background): “[23] Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Chris Paxton, and Dieter Fox.”
“Good Robot!”: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer (2019, background): “6-DOF GraspNet [21] uses simulated grasp data to generalize to new objects and has been extended to handle reliable grasping of novel objects in clutter [12].”
Unseen Object Instance Segmentation for Robotic Environments (2020, methodology): “Building such a robust object recognition module is valuable for robots interacting with objects, such as picking up unseen objects or learning to use new tools [1], [2], [3].”
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review (2020, background): “[Murali et al., 2019] further improved 6-DoF GraspNet by introducing a learned collision checker conditioned on the gripper information and on the raw point cloud of the scene, which affords a higher success rate in cluttered scenes.”
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes (2021, methodology): “This is 10% higher than [12] in equal settings.”
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation (2020, background): “However, there does not exist a large scale dataset of real-world images for many objects in tabletop scenes.”
A Joint Network for Grasp Detection Conditioned on Natural Language Commands (2021, background): “While classical approaches are based on mechanical constrains [14], [15], recent studies resorted to CNN to capture the geometric information to either predict quality scores for sampled grasps [16], to directly output the grasp configurations [17]–[20], or to capture collision information [21].”
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation (2022, background): “While other works in 6-DoF grasping [30, 31, 32, 33, 34] have used RGB-D and pointcloud input, they have not been applied to sequential tasks or used with language-conditioning.”