fasadmoon.blogg.se - Where are blocks saved in tds foresight

#Where are blocks saved in tds foresight install#
#Where are blocks saved in tds foresight code#
#Where are blocks saved in tds foresight series#
#Where are blocks saved in tds foresight simulator#

Thus, we offer a utility in visual_mpc/utils/file_2_record.py which converts data from our raw format to compressed TFRecords. While the raw (pkl/jpeg file) data format is convenient to work with, it is far less efficient for model training.

To collect data with l-block objects and autograsp (x, y, z, wrist rotation, grasp reflex) action space run: python visual_mpc/sim/run.py data_collection/sim/grasp_reflex_lblocks/hparams.py -nworkers.

Use visual_mpc/sim/run.py to start random data collection in our custom MuJoCo cartgripper environment

For deformable object collection: rosrun foresight_rospkg run_robot.py data_collection/sawyer/towel_data/hparams.py -r.

For hard object collection: rosrun foresight_rospkg run_robot.py data_collection/sawyer/hard_object_data/hparams.py -r.

Use run_robot to start random data collection on the Sawyer.

#Where are blocks saved in tds foresight series#

Rollouts are saved as a series of pickled dictionaries and JPEG images, or as compressed TFRecords. Data Collectionīy default data is saved in the same directory as the corresponding python config file. Similarly, rosrun foresight_rospkg run_robot.py is the primary entry point for the robot experiments/data-collection. The correct configuration file must be supplied, for each experiment/data collection run. In sim, data collection and benchmarks are started by running python visual_mpc/sim/run.py. Rosrun foresight_rospkg send_urdf_fragment.py # (optional) stop after Sawyer recognizes the gripper Roslaunch foresight_rospkg start_gripper.launch # start gripper node Roslaunch foresight_rospkg start_cameras.launch # run cameras

#Where are blocks saved in tds foresight install#

Remember to install our python packages by running sudo python setup.py develop in EVERY project workspace.

Clone and install the meta-classifier code-base.

Clone and install the video_prediction code-base.

Then run catkin_make to rebuild your workspace.

Clone our repository into your ROS workspace's src folder.

We use a modified version of video_stream_opencv Assuming you use our same hardware, you will need to install the following: The robot is filmed from two orthogonal viewing angles using consumer webcams. Now to create a new bash in this environment run: nvidia-docker run -it foresight/sim: bash Robot Hardware SetupĪll experiments are conducted on a Sawyer robot with an attached WSG-50 gripper. Nvidia-docker build -t foresight/sim:latest. Git clone & cd docker & cp ~/.mujoco/mjkey.txt. After you setup Python and MuJoCo, installation directions are as follows:

We strongly recommend using a virtual environment (such as Anaconda) for this project.

#Where are blocks saved in tds foresight simulator#

Our simulator requires Python 3.5.2 and MuJoCo 1.5 to run successfully.

#Where are blocks saved in tds foresight code#

Since this project is deployed in sim and on a robot, all code is written to be compatible with Python 2.7 and Python 3.5. If you're only interested in training models, please refer to Stochastic Adversarial Video Prediction and/or Few-Shot Goal Inference for Visuomotor Learning and Planning. This codebase provides an implmentation of: unsupervised data collection, our benchmarking framework, the various planning costs, and - of course - the visual-MPC controller! Additionally, we provide: instructions to reproduce our experiments, Dockerfiles for our simulator environments, and documentation on our Sawyer robot setup.Ĭrucially, this codebase does NOT implement video prediction model training, or meta-classifier model training. On a high level, Visual Model Predictive Control (visual-MPC) leverages an action-conditioned video prediction model (trained from unsupervised interaction) to enable robots to perform various tasks with only raw-pixel input. Code for reproducing experiments in Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control, along with expanded support for additional robots.