catch_carry

Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks

This package contains motion capture data and tasks associated with "Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks" (https://arxiv.org/abs/1911.06636), which was published at SIGGRAPH 2020. This is research code, and has dependencies on more stable code that is available as part of dm_control, in particular upon components in dm_control.locomotion.

To get access to preconfigured python environments for the "warehouse" and "ball toss" tasks, see the task_examples.py file. To use the MuJoCo interactive viewer (from dm_control) to load the environments, see explore.py.

Installation instructions

1. Download MuJoCo Pro and extract the zip archive as ~/.mujoco/mujoco200_$PLATFORM where $PLATFORM is one of linux, macos, or win64.

2. Ensure that a valid MuJoCo license key file is located at ~/.mujoco/mjkey.txt.

3. Clone the deepmind-research repository:

      git clone https://github.com/deepmind/deepmind-research.git
      cd deepmind-research

4. Create and activate a Python virtual environment:

      python3 -m virtualenv catch_carry
      source catch_carry/bin/activate

5. Install the package:

      pip install ./catch_carry

Quickstart

To instantiate and step through the warehouse task:

from catch_carry import task_examples
import numpy as np

# Build an example environment.
env = task_examples.build_vision_warehouse()

# Get the `action_spec` describing the control inputs.
action_spec = env.action_spec()

# Step through the environment for one episode with random actions.
time_step = env.reset()
while not time_step.last():
  action = np.random.uniform(action_spec.minimum, action_spec.maximum,
                             size=action_spec.shape)
  time_step = env.step(action)
  print("reward = {}, discount = {}, observations = {}.".format(
      time_step.reward, time_step.discount, time_step.observation))

The above code snippet can also be used for the ball toss task by replacing build_vision_warehouse with build_vision_toss.

Visualization

dm_control.viewer can be used to visualize and interact with the environment. We provide the explore.py script specifically for this. If you followed our installation instructions above, this can be launched for the warehouse task via:

python3 -m catch_carry.explore --task=warehouse

and for the ball toss task via:

python3 -m catch_carry.explore --task=toss

Citation

If you use the code or data in this package, please cite:

@article{merel2020catch,
    title = {Catch \& Carry: Reusable Neural Controllers for
             Vision-Guided Whole-Body Tasks},
    author = {Merel, Josh and
              Tunyasuvunakool, Saran and
              Ahuja, Arun and
              Tassa, Yuval and
              Hasenclever, Leonard and
              Pham, Vu and
              Erez, Tom and
              Wayne, Greg and
              Heess, Nicolas},
    journal = {ACM Trans. Graph.},
    issue_date = {July 2020},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    volume = {39},
    number = {4},
    articleno = {39},
    numpages = {14},
    issn = {0730-0301},
    year = {2020},
    month = jul,
    url = {https://doi.org/10.1145/3386569.3392474},
    doi = {10.1145/3386569.3392474},
}