https://github.com/berkeleyautomation/fast_grasp_detect
For supporting our IL_ROS_HSR code
https://github.com/berkeleyautomation/fast_grasp_detect
Last synced: 2 months ago
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For supporting our IL_ROS_HSR code
- Host: GitHub
- URL: https://github.com/berkeleyautomation/fast_grasp_detect
- Owner: BerkeleyAutomation
- Created: 2018-10-10T19:22:14.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-10-10T19:24:33.000Z (over 6 years ago)
- Last Synced: 2025-01-25T21:26:37.502Z (4 months ago)
- Language: Python
- Size: 36.1 KB
- Stars: 1
- Watchers: 6
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# fast_grasp_detect
Used to support our IL_ROS_HSR code.
For the bed-making project code, [see this snapshot in a separate repository][4].
## install
```
python setup.py develop
```
(Or `pip install -e .` in this directory ... that might be better)
## main
Contains scripts that can be run to train the networks. We use two networks, one
for the grasping ("grasp network") and one for the transition policy ("success
network").
- `train_bed_grasp.py` - trains the grasping network ([hyperparameters here][1])
- `train_bed_success.py` - trains the network that identifies successful
rollouts ([hyperparameters here][2])
Both of these call `solver = Solver(bed_grasp_options,yolo,pascal)` and then
`solver.train()` to train the network, based on `core/train_network.py`. The
difference is that `yolo` can be the output from `networks/grasp_net_cs.py` or
`networks/success_net.py`. The `pascal` portion handles the YOLO pre-trained
features, so that the two networks can simply fine-tune.
The networks use TF slim. [Documentation here][3].
**Probably better to run for now**: use the scripts `main/grasp.sh` and `main/success.sh` since
these iterate through all indices in a cross validation set, and then put the output to a file, such
as in:
```
./main/grasp.sh | tee logs/grasp.log
```
so that we can inspect the output later.
## src
(This is older stuff from Michael's documentation)
• CONFIG
○ Bed_grasp_config = grasp network (trains the hsr to use end-effector to make an effective grasp)
○ Bed_success_config = success network (tells you whether the sequence of states and actions resulted in a successfully made bed)
○ tl_experiments/ = contains training scripts for transfer learning experiments
○ Other config files are for different bed-making experiments.
• CORE
○ Data_manager loads the yolo network and can be used to get the data as an array of images and labels
○ Grasp_data.py is a data_manager for only the management of the grasp-net data
○ Timer.py has 3 methods. Tic records the start time, toc returns the difference from the start time or the average time. Remain returns the time at which the timer runs out
○ Train_fast.py trains the grasp network fast using a Solver class that takes in a network (yolo) and the dataset (pascal)
○ Yolo_conv_features_cs.py makes the yolo CNN (builds or restores from a given weights file)
§ Also extracts features from an image using a tensorflow feature extraction function
○ Train_network.py = used to train the yolo network
• DATA_AUG
○ Augment_lighting has functions to add lighting noise to images that will later be used as training inputs
○ Data_augment.py goes through the images and adds lighting-noisy and flipped images to the data set
• DETECTORS
○ Grasp_detector.py = Detects the optimal grasp point given an image of the bed
§ Returns that grasp as a pose, which is a set of x,y coordinates that can be manipulated as necessary
○ Tran_detector.py = detects whether the image of the bed is ready for a transition to the next robot position
§ Detects if the robot has successfully done all it can from a spot on the floor
• LABELERS
○ Labeler.py = puts bounding boxes on an image upon loading it [offline]
○ Online_labeler.py = recomputes bounding boxes after an action is completed
• NETWORKS
○ Grasp_net_cs.py = builds the entire grasp net
○ Success_net.py = builds the success net (whether the bed is made or not)
• VISUALIZERS
○ Draw_cross_hair.py = used to draw an x on top of some location on the given image
[1]:https://github.com/DanielTakeshi/fast_grasp_detect/blob/master/src/fast_grasp_detect/configs/bed_grasp_config.py
[2]:https://github.com/DanielTakeshi/fast_grasp_detect/blob/master/src/fast_grasp_detect/configs/bed_success_config.py
[3]:https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim
[4]:https://github.com/DanielTakeshi/fast_grasp_detect