IEEE Robotics & Automation Magazine - June 2020 - 59
mixture component predicted by the MDN for several different objects.
.github.io/). We used the built-in Gazebo Kinect camera to
generate pointclouds simulating the Kinect2 RGB-D camera
we used in real-world experiments. All data and software
used in this article are available online (https://robot-learning
.cs.utah.edu/project/grasp_voxel_inference).
We collected training data using a heuristic, geometrybased grasp planner adapted from our previous planner in Lu
et al. [24], which is quite similar to the geometric primitive
planner of Miller et al. [33] for boxes or cylinders. We collected both side and overhead multifingered grasps. For side
grasps, we had the thumb pointing toward the top as in Lu et al.
Grasp-Success
Probability
fc 32, relu
fc 64, relu
+
fc 64, relu
fc 128, relu
fc 343, elu
3 × 3 × 3 conv3D, 32
Object
Voxel
Grid
+
Object Size [w, h,d]
fc 8, relu
3 × 3 × 3 conv3D, 16, /2
Grasp
Configuration
3 × 3 × 3 conv3D, 64, /2
fc 14, relu
3 × 3 × 3 conv3D, 8
Grasp Data Collection
We conducted all training and experiments using the fourfingered, 16-degrees-of-freedom (DoF) Allegro hand-mounted on a Kuka LBR4 7-DoF arm. We used a Kinect2 camera to
generate the pointcloud of the object on the table. We collected simulated grasp data using our robot hand-arm setup
inside the Gazebo simulator with the Dynamic Animation
and Robotics Toolkit physics engine (https://dartsim
Voxel Encoder
(a)
Mixture Distribution
fc 32, relu
fc 128, relu
fc 343, relu
3 × 3 × 3 conv3D, 64, /2
3 × 3 × 3 conv3D, 32
3 × 3 × 3 conv3D, 16, /2
+
Object
Voxel
Grid
3 × 3 × 3 conv3D, 8
Object Size [w, h,d]
Voxel Encoder
(b)
Figure 1. The voxel-config-net and MDN architectures. (a) The architecture of our voxel-config-net for grasp success probability
prediction. (b) The architecture of our MDN modeling the grasp conditional prior. The image labeled "Object Voxel Grid" shows the
voxel grid for the mustard object. All convolutional layers use 3 # 3 # 3 3D convolutional filters with exponential linear unit (elu)
activations. We annotated the number of filters and the stride (/2 means a stride of 2) for the convolutional layers. We annotated
the number of neurons and the activation function for fully connected layers. relu: rectified linear activation unit; fc: fully connected;
conv3D: convolutional 3D.
JUNE 2020
*
IEEE ROBOTICS & AUTOMATION MAGAZINE
*
59
https://dartsim.github.io/
https://robot-learning.cs.utah.edu/project/grasp_voxel_inference
https://robot-learning.cs.utah.edu/project/grasp_voxel_inference
https://dartsim.github.io/
IEEE Robotics & Automation Magazine - June 2020
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