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environment/setup_same_hardware.sh

@@ -5,6 +5,8 @@ ROOT="${1:-/workspace}"
 MINIFORGE_DIR="${ROOT}/miniforge3"
 ENV_PREFIX="${MINIFORGE_DIR}/envs/rlbench"
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
+EXTERNAL_ROOT="${REPO_ROOT}/external"
 
 echo "[1/6] optional system packages"
 echo "Install these on Ubuntu 22.04 if they are missing:"
@@ -28,25 +30,32 @@ else
 fi
 conda activate "${ENV_PREFIX}"
 
-echo "[4/6] clone exact upstream repos"
+echo "[4/6] install benchmark source trees"
 mkdir -p "${ROOT}"
-if [[ ! -d "${ROOT}/rlbench/.git" ]]; then
-  git clone https://github.com/markusgrotz/RLBench.git "${ROOT}/rlbench"
-fi
-if [[ ! -d "${ROOT}/pyrep/.git" ]]; then
-  git clone https://github.com/markusgrotz/PyRep.git "${ROOT}/pyrep"
-fi
-if [[ ! -d "${ROOT}/peract_bimanual/.git" ]]; then
-  git clone https://github.com/markusgrotz/peract_bimanual.git "${ROOT}/peract_bimanual"
-fi
-if [[ ! -d "${ROOT}/yarr/.git" ]]; then
-  git clone https://github.com/markusgrotz/YARR.git "${ROOT}/yarr"
-fi
+if [[ -d "${EXTERNAL_ROOT}/rlbench" ]]; then
+  rsync -a "${EXTERNAL_ROOT}/rlbench/" "${ROOT}/rlbench/"
+  rsync -a "${EXTERNAL_ROOT}/pyrep/" "${ROOT}/pyrep/"
+  rsync -a "${EXTERNAL_ROOT}/peract_bimanual/" "${ROOT}/peract_bimanual/"
+  rsync -a "${EXTERNAL_ROOT}/yarr/" "${ROOT}/yarr/"
+else
+  if [[ ! -d "${ROOT}/rlbench/.git" ]]; then
+    git clone https://github.com/markusgrotz/RLBench.git "${ROOT}/rlbench"
+  fi
+  if [[ ! -d "${ROOT}/pyrep/.git" ]]; then
+    git clone https://github.com/markusgrotz/PyRep.git "${ROOT}/pyrep"
+  fi
+  if [[ ! -d "${ROOT}/peract_bimanual/.git" ]]; then
+    git clone https://github.com/markusgrotz/peract_bimanual.git "${ROOT}/peract_bimanual"
+  fi
+  if [[ ! -d "${ROOT}/yarr/.git" ]]; then
+    git clone https://github.com/markusgrotz/YARR.git "${ROOT}/yarr"
+  fi
 
-git -C "${ROOT}/rlbench" checkout 8af748c51287989294e00c9c670e3330a0e35ed5
-git -C "${ROOT}/pyrep" checkout b8bd1d7a3182adcd570d001649c0849047ebf197
-git -C "${ROOT}/peract_bimanual" checkout bb0232a6ba3fe116566e9568f0c7af980ed6703d
-git -C "${ROOT}/yarr" checkout 6822ff78602c77878b27d4cfe759ce029c67bffb
+  git -C "${ROOT}/rlbench" checkout 8af748c51287989294e00c9c670e3330a0e35ed5
+  git -C "${ROOT}/pyrep" checkout b8bd1d7a3182adcd570d001649c0849047ebf197
+  git -C "${ROOT}/peract_bimanual" checkout bb0232a6ba3fe116566e9568f0c7af980ed6703d
+  git -C "${ROOT}/yarr" checkout 6822ff78602c77878b27d4cfe759ce029c67bffb
+fi
 
 echo "[5/6] install editable repos used by the rlbench env"
 python -m pip install -e "${ROOT}/pyrep"

external/rlbench/.github/workflows/task_tests.yml

@@ -0,0 +1,52 @@
+name: Task Tests
+
+# Controls when the action will run.
+# Run this workflow every time a new commit pushed to your repository
+on:
+  # Triggers the workflow on push or pull request events.
+  push:
+  pull_request:
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "build"
+  build:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-20.04
+    env:
+      DISPLAY: :0
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - uses: actions/checkout@v2
+
+      - name: Test
+        run: |
+          sudo apt-get update -qq
+          sudo apt-get install -y xvfb qtbase5-dev qtdeclarative5-dev libqt5webkit5-dev libsqlite3-dev qt5-default qttools5-dev-tools
+          # start xvfb in the background
+          sudo /usr/bin/Xvfb $DISPLAY -screen 0 1280x1024x24 &
+          cur=`pwd`
+          wget http://www.coppeliarobotics.com/files/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz
+          tar -xf CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz
+          export COPPELIASIM_ROOT="$cur/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04"
+          export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$COPPELIASIM_ROOT:$COPPELIASIM_ROOT/platforms
+          export QT_QPA_PLATFORM_PLUGIN_PATH=$COPPELIASIM_ROOT
+
+          # Install PyRep
+          git clone https://github.com/markusgrotz/PyRep.git
+          cd PyRep
+          pip3 install -r requirements.txt
+          pip3 install setuptools
+          pip3 install .
+
+          # Gym needed for some of unit tests
+          pip3 install gym
+
+          cd ../
+          pip3 install -r requirements.txt
+          python3 -m unittest discover tests/demos

external/rlbench/.github/workflows/unit_tests.yml

@@ -0,0 +1,52 @@
+name: Unit Tests
+
+# Controls when the action will run.
+# Run this workflow every time a new commit pushed to your repository
+on:
+  # Triggers the workflow on push or pull request events.
+  push:
+  pull_request:
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "build"
+  build:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-20.04
+    env:
+      DISPLAY: :0
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - uses: actions/checkout@v2
+
+      - name: Test
+        run: |
+          sudo apt-get update -qq
+          sudo apt-get install -y xvfb qtbase5-dev qtdeclarative5-dev libqt5webkit5-dev libsqlite3-dev qt5-default qttools5-dev-tools
+          # start xvfb in the background
+          sudo /usr/bin/Xvfb $DISPLAY -screen 0 1280x1024x24 &
+          cur=`pwd`
+          wget http://www.coppeliarobotics.com/files/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz
+          tar -xf CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz
+          export COPPELIASIM_ROOT="$cur/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04"
+          export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$COPPELIASIM_ROOT:$COPPELIASIM_ROOT/platforms
+          export QT_QPA_PLATFORM_PLUGIN_PATH=$COPPELIASIM_ROOT
+
+          # Install PyRep
+          git clone https://github.com/markusgrotz/PyRep.git
+          cd PyRep
+          pip3 install -r requirements.txt
+          pip3 install setuptools
+          pip3 install .
+
+          # Gym needed for some of unit tests
+          pip3 install gym
+
+          cd ../
+          pip3 install -r requirements.txt
+          python3 -m unittest discover tests/unit

external/rlbench/examples/custom_action_mode.py

@@ -0,0 +1,61 @@
+import numpy as np
+
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.backend.scene import Scene
+from rlbench.environment import Environment
+from rlbench.observation_config import ObservationConfig
+from rlbench.tasks import ReachTarget
+
+
+class CustomAbsoluteJointVelocity(JointVelocity):
+
+    def action(self, scene: Scene, action: np.ndarray):
+        if np.random.random() > 0.5:
+            # Example of custom behaviour.
+            # Here we randomly ignore 50% of actions
+            print('Skip action!')
+            return
+        super(CustomAbsoluteJointVelocity, self).action(scene, action)
+
+
+class Agent(object):
+
+    def __init__(self, action_shape):
+        self.action_shape = action_shape
+
+    def act(self, obs):
+        arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
+        gripper = [1.0]  # Always open
+        return np.concatenate([arm, gripper], axis=-1)
+
+
+action_mode = MoveArmThenGripper(
+    arm_action_mode=CustomAbsoluteJointVelocity(),
+    gripper_action_mode=Discrete())
+
+env = Environment(
+    action_mode,
+    obs_config=ObservationConfig(),
+    headless=False)
+env.launch()
+
+task = env.get_task(ReachTarget)
+
+agent = Agent(env.action_shape)
+
+training_steps = 120
+episode_length = 40
+obs = None
+for i in range(training_steps):
+    if i % episode_length == 0:
+        print('Reset Episode')
+        descriptions, obs = task.reset()
+        print(descriptions)
+    action = agent.act(obs)
+    print(action)
+    obs, reward, terminate = task.step(action)
+
+print('Done')
+env.shutdown()

external/rlbench/examples/few_shot_rl.py

@@ -0,0 +1,56 @@
+import numpy as np
+
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.environment import Environment
+from rlbench.observation_config import ObservationConfig
+from rlbench.tasks import FS10_V1
+
+
+class Agent(object):
+
+    def __init__(self, action_shape):
+        self.action_shape = action_shape
+
+    def act(self, obs):
+        arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
+        gripper = [1.0]  # Always open
+        return np.concatenate([arm, gripper], axis=-1)
+
+
+obs_config = ObservationConfig()
+obs_config.set_all(True)
+
+env = Environment(
+    action_mode=MoveArmThenGripper(
+        arm_action_mode=JointVelocity(), gripper_action_mode=Discrete()),
+    obs_config=ObservationConfig(),
+    headless=False)
+env.launch()
+
+agent = Agent(env.action_shape)
+
+train_tasks = FS10_V1['train']
+test_tasks = FS10_V1['test']
+
+training_cycles_per_task = 3
+training_steps_per_task = 80
+episode_length = 40
+
+for _ in range(training_cycles_per_task):
+
+    task_to_train = np.random.choice(train_tasks, 1)[0]
+    task = env.get_task(task_to_train)
+    task.sample_variation()  # random variation
+
+    for i in range(training_steps_per_task):
+        if i % episode_length == 0:
+            print('Reset Episode')
+            descriptions, obs = task.reset()
+            print(descriptions)
+        action = agent.act(obs)
+        obs, reward, terminate = task.step(action)
+
+print('Done')
+env.shutdown()

external/rlbench/examples/imitation_learning.py

@@ -0,0 +1,51 @@
+import numpy as np
+
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.environment import Environment
+from rlbench.observation_config import ObservationConfig
+from rlbench.tasks import ReachTarget
+
+
+class ImitationLearning(object):
+
+    def predict_action(self, batch):
+        return np.random.uniform(size=(len(batch), 7))
+
+    def behaviour_cloning_loss(self, ground_truth_actions, predicted_actions):
+        return 1
+
+
+# To use 'saved' demos, set the path below, and set live_demos=False
+live_demos = True
+DATASET = '' if live_demos else 'PATH/TO/YOUR/DATASET'
+
+obs_config = ObservationConfig()
+obs_config.set_all(True)
+
+env = Environment(
+    action_mode=MoveArmThenGripper(
+        arm_action_mode=JointVelocity(), gripper_action_mode=Discrete()),
+    obs_config=ObservationConfig(),
+    headless=False)
+env.launch()
+
+task = env.get_task(ReachTarget)
+
+il = ImitationLearning()
+
+demos = task.get_demos(2, live_demos=live_demos)  # -> List[List[Observation]]
+demos = np.array(demos).flatten()
+
+# An example of using the demos to 'train' using behaviour cloning loss.
+for i in range(100):
+    print("'training' iteration %d" % i)
+    batch = np.random.choice(demos, replace=False)
+    batch_images = [obs.left_shoulder_rgb for obs in batch]
+    predicted_actions = il.predict_action(batch_images)
+    ground_truth_actions = [obs.joint_velocities for obs in batch]
+    loss = il.behaviour_cloning_loss(ground_truth_actions, predicted_actions)
+
+print('Done')
+env.shutdown()

external/rlbench/examples/multi_task_rl.py

@@ -0,0 +1,55 @@
+import numpy as np
+
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.environment import Environment
+from rlbench.observation_config import ObservationConfig
+from rlbench.tasks import MT30_V1
+
+
+class Agent(object):
+
+    def __init__(self, action_shape):
+        self.action_shape = action_shape
+
+    def act(self, obs):
+        arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
+        gripper = [1.0]  # Always open
+        return np.concatenate([arm, gripper], axis=-1)
+
+
+obs_config = ObservationConfig()
+obs_config.set_all(True)
+
+env = Environment(
+    action_mode=MoveArmThenGripper(
+        arm_action_mode=JointVelocity(), gripper_action_mode=Discrete()),
+    obs_config=ObservationConfig(),
+    headless=False)
+env.launch()
+
+agent = Agent(env.action_shape)
+
+train_tasks = MT30_V1['train']
+
+training_cycles_per_task = 3
+training_steps_per_task = 80
+episode_length = 40
+
+for _ in range(training_cycles_per_task):
+
+    task_to_train = np.random.choice(train_tasks, 1)[0]
+    task = env.get_task(task_to_train)
+    task.sample_variation()  # random variation
+
+    for i in range(training_steps_per_task):
+        if i % episode_length == 0:
+            print('Reset Episode')
+            descriptions, obs = task.reset()
+            print(descriptions)
+        action = agent.act(obs)
+        obs, reward, terminate = task.step(action)
+
+print('Done')
+env.shutdown()

external/rlbench/examples/rearrangement_challenge.py

@@ -0,0 +1,67 @@
+"""
+This is a simple starting point for the rearrangement challenge mentioned
+in .....
+
+"""
+import numpy as np
+
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.environment import Environment
+from rlbench.observation_config import ObservationConfig
+from rlbench.tasks import PutAllGroceriesInCupboard
+
+
+class Agent(object):
+    """A simple random-action agent. """
+
+    def __init__(self, action_shape):
+        self.action_shape = action_shape
+
+    def act(self, obs):
+        arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
+        gripper = [1.0]  # Always open
+        return np.concatenate([arm, gripper], axis=-1)
+
+
+# Define the observations that we want to get at each timestep.
+obs_config = ObservationConfig()
+obs_config.set_all(True)
+
+# Define the action mode of the arm. There are many to choose from.
+action_mode = MoveArmThenGripper(
+    arm_action_mode=JointVelocity(), gripper_action_mode=Discrete())
+
+# Create and launch the RLBench environment.
+env = Environment(
+    action_mode, obs_config=obs_config, headless=False)
+env.launch()
+
+# Get the task that we want to interface with. There are >100 tasks to choose!
+# For the rearrangement challenge, we want 'PutAllGroceriesInCupboard'.
+task = env.get_task(PutAllGroceriesInCupboard)
+
+# Uncomment line below to get 'live' demonstrations of this task!
+# demos = task.get_demos(1)
+
+# Create our simple agent
+agent = Agent(env.action_shape)
+
+training_steps = 120
+episode_length = 40
+for i in range(training_steps):
+    if i % episode_length == 0:
+        print('Reset Episode')
+        # When we reset the task, we get given a list of strings that describe
+        # the task and an initial observation
+        descriptions, obs = task.reset()
+        print(descriptions)
+    # Using the current observation, use an agent to decide on the next action
+    action = agent.act(obs)
+    print(action)
+    # Step the task and obtain a new observation, reward and a terminate flag.
+    obs, reward, terminate = task.step(action)
+
+print('Done')
+env.shutdown()

external/rlbench/examples/rlbench_gym.py

@@ -0,0 +1,16 @@
+import gym
+import rlbench.gym
+
+env = gym.make('reach_target-state-v0', render_mode='human')
+
+training_steps = 120
+episode_length = 40
+for i in range(training_steps):
+    if i % episode_length == 0:
+        print('Reset Episode')
+        obs = env.reset()
+    obs, reward, terminate, _ = env.step(env.action_space.sample())
+    env.render()  # Note: rendering increases step time.
+
+print('Done')
+env.close()

external/rlbench/examples/single_task_rl.py

@@ -0,0 +1,45 @@
+import numpy as np
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.environment import Environment
+from rlbench.observation_config import ObservationConfig
+from rlbench.tasks import ReachTarget
+
+
+class Agent(object):
+
+    def __init__(self, action_shape):
+        self.action_shape = action_shape
+
+    def act(self, obs):
+        arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
+        gripper = [1.0]  # Always open
+        return np.concatenate([arm, gripper], axis=-1)
+
+
+env = Environment(
+    action_mode=MoveArmThenGripper(
+        arm_action_mode=JointVelocity(), gripper_action_mode=Discrete()),
+    obs_config=ObservationConfig(),
+    headless=False)
+env.launch()
+
+task = env.get_task(ReachTarget)
+
+agent = Agent(env.action_shape)
+
+training_steps = 120
+episode_length = 40
+obs = None
+for i in range(training_steps):
+    if i % episode_length == 0:
+        print('Reset Episode')
+        descriptions, obs = task.reset()
+        print(descriptions)
+    action = agent.act(obs)
+    print(action)
+    obs, reward, terminate = task.step(action)
+
+print('Done')
+env.shutdown()

external/rlbench/examples/single_task_rl_domain_randomization.py

@@ -0,0 +1,56 @@
+import numpy as np
+
+from rlbench import Environment
+from rlbench import ObservationConfig
+from rlbench import RandomizeEvery
+from rlbench import VisualRandomizationConfig
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.tasks import ReachTarget
+
+
+class Agent(object):
+
+    def __init__(self, action_shape):
+        self.action_shape = action_shape
+
+    def act(self, obs):
+        arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
+        gripper = [1.0]  # Always open
+        return np.concatenate([arm, gripper], axis=-1)
+
+
+obs_config = ObservationConfig()
+obs_config.set_all(True)
+
+# We will borrow some from the tests dir
+rand_config = VisualRandomizationConfig(
+    image_directory='../tests/unit/assets/textures')
+
+action_mode = MoveArmThenGripper(
+        arm_action_mode=JointVelocity(), gripper_action_mode=Discrete())
+env = Environment(
+    action_mode, obs_config=obs_config, headless=False,
+    randomize_every=RandomizeEvery.EPISODE, frequency=1,
+    visual_randomization_config=rand_config
+)
+env.launch()
+
+task = env.get_task(ReachTarget)
+
+agent = Agent(env.action_shape)
+
+training_steps = 120
+episode_length = 20
+obs = None
+for i in range(training_steps):
+    if i % episode_length == 0:
+        print('Reset Episode')
+        descriptions, obs = task.reset()
+        print(descriptions)
+    action = agent.act(obs)
+    obs, reward, terminate = task.step(action)
+
+print('Done')
+env.shutdown()

external/rlbench/examples/single_task_rl_with_demos.py

@@ -0,0 +1,57 @@
+import numpy as np
+
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.environment import Environment
+from rlbench.observation_config import ObservationConfig
+from rlbench.tasks import ReachTarget
+
+
+class Agent(object):
+
+    def __init__(self, action_shape):
+        self.action_shape = action_shape
+
+    def ingest(self, demos):
+        pass
+
+    def act(self, obs):
+        arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
+        gripper = [1.0]  # Always open
+        return np.concatenate([arm, gripper], axis=-1)
+
+
+# To use 'saved' demos, set the path below, and set live_demos=False
+live_demos = True
+DATASET = '' if live_demos else 'PATH/TO/YOUR/DATASET'
+
+obs_config = ObservationConfig()
+obs_config.set_all(True)
+
+action_mode = MoveArmThenGripper(
+    arm_action_mode=JointVelocity(), gripper_action_mode=Discrete())
+env = Environment(
+    action_mode, DATASET, obs_config, False)
+env.launch()
+
+task = env.get_task(ReachTarget)
+demos = task.get_demos(2, live_demos=live_demos)
+
+agent = Agent(env.action_shape)
+agent.ingest(demos)
+
+training_steps = 120
+episode_length = 40
+obs = None
+for i in range(training_steps):
+    if i % episode_length == 0:
+        print('Reset Episode')
+        descriptions, obs = task.reset()
+        print(descriptions)
+    action = agent.act(obs)
+    print(action)
+    obs, reward, terminate = task.step(action)
+
+print('Done')
+env.shutdown()

external/rlbench/examples/swap_arm.py

@@ -0,0 +1,50 @@
+import numpy as np
+
+from rlbench.action_modes.action_mode import MoveArmThenGripper
+from rlbench.action_modes.arm_action_modes import JointVelocity
+from rlbench.action_modes.gripper_action_modes import Discrete
+from rlbench.environment import Environment
+from rlbench.observation_config import ObservationConfig
+from rlbench.tasks import ReachTarget
+
+
+class Agent(object):
+
+    def __init__(self, action_shape):
+        self.action_shape = action_shape
+
+    def act(self, obs):
+        arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
+        gripper = [1.0]  # Always open
+        return np.concatenate([arm, gripper], axis=-1)
+
+
+obs_config = ObservationConfig()
+obs_config.set_all(True)
+obs_config.gripper_touch_forces = False
+
+action_mode = MoveArmThenGripper(
+    arm_action_mode=JointVelocity(), gripper_action_mode=Discrete())
+env = Environment(
+    action_mode, obs_config=obs_config, headless=False,
+    robot_setup='sawyer')
+env.launch()
+
+task = env.get_task(ReachTarget)
+
+agent = Agent(env.action_shape)  # 6DoF + 1 for gripper
+
+training_steps = 120
+episode_length = 40
+obs = None
+for i in range(training_steps):
+    if i % episode_length == 0:
+        print('Reset Episode')
+        descriptions, obs = task.reset()
+        print(descriptions)
+    action = agent.act(obs)
+    print(action)
+    obs, reward, terminate = task.step(action)
+
+print('Done')
+env.shutdown()

external/rlbench/rlbench/action_modes/action_mode.py

@@ -0,0 +1,164 @@
+from abc import abstractmethod
+
+import numpy as np
+
+from rlbench.action_modes.arm_action_modes import ArmActionMode
+from rlbench.action_modes.arm_action_modes import BimanualJointPosition, JointPosition
+from rlbench.action_modes.gripper_action_modes import GripperActionMode
+from rlbench.action_modes.gripper_action_modes import BimanualGripperJointPosition, GripperJointPosition
+from rlbench.action_modes.gripper_action_modes import BimanualDiscrete
+from rlbench.backend.scene import Scene
+
+
+class ActionMode(object):
+
+    def __init__(self,
+                 arm_action_mode: 'ArmActionMode',
+                 gripper_action_mode: 'GripperActionMode'):
+        self.arm_action_mode = arm_action_mode
+        self.gripper_action_mode = gripper_action_mode
+
+    @abstractmethod
+    def action(self, scene: Scene, action: np.ndarray):
+        pass
+
+    @abstractmethod
+    def action_shape(self, scene: Scene):
+        pass
+
+    def action_bounds(self):
+        """Returns the min and max of the action mode."""
+        raise NotImplementedError('You must define your own action bounds.')
+
+
+
+
+class MoveArmThenGripper(ActionMode):
+    """A customizable action mode.
+
+    The arm action is first applied, followed by the gripper action.
+    """
+
+    def action(self, scene: Scene, action: np.ndarray):
+        arm_act_size = np.prod(self.arm_action_mode.action_shape(scene))
+        arm_action = np.array(action[:arm_act_size])
+        ee_action = np.array(action[arm_act_size:arm_act_size+1])
+        ignore_collisions = bool(action[arm_act_size+1:arm_act_size+2])
+        self.arm_action_mode.action(scene, arm_action, ignore_collisions)
+        self.gripper_action_mode.action(scene, ee_action)
+
+    def action_shape(self, scene: Scene):
+        return np.prod(self.arm_action_mode.action_shape(scene)) + np.prod(
+            self.gripper_action_mode.action_shape(scene))
+
+
+
+class BimanualMoveArmThenGripper(MoveArmThenGripper):
+    """The arm action is first applied, followed by the gripper action. """
+
+    def action(self, scene: Scene, action: np.ndarray):
+
+        assert(len(action) == 18)
+
+        arm_action_size = np.prod(self.arm_action_mode.unimanual_action_shape(scene))
+        ee_action_size = np.prod(self.gripper_action_mode.unimanual_action_shape(scene))
+        ignore_collisions_size = 1
+
+        action_size = arm_action_size + ee_action_size + ignore_collisions_size
+
+        assert(action_size == 9)
+
+        right_action = action[:action_size]
+        left_action = action[action_size:]
+
+        right_arm_action = np.array(right_action[:arm_action_size])
+        left_arm_action = np.array(left_action[:arm_action_size])
+
+        arm_action = np.concatenate([right_arm_action, left_arm_action], axis=0)        
+
+        right_ee_action = np.array(right_action[arm_action_size:arm_action_size+ee_action_size])
+        left_ee_action = np.array(left_action[arm_action_size:arm_action_size+ee_action_size])
+        ee_action = np.concatenate([right_ee_action, left_ee_action], axis=0)
+
+        right_ignore_collisions = bool(right_action[arm_action_size+ee_action_size:arm_action_size+ee_action_size+1])
+        left_ignore_collisions = bool(left_action[arm_action_size+ee_action_size:arm_action_size+ee_action_size+1])
+        ignore_collisions = [right_ignore_collisions, left_ignore_collisions]
+
+        self.arm_action_mode.action(scene, arm_action, ignore_collisions)
+        self.gripper_action_mode.action(scene, ee_action)
+
+
+    def action_shape(self, scene: Scene):
+        return np.prod(self.arm_action_mode.action_shape(scene)) + np.prod(
+            self.gripper_action_mode.action_shape(scene)) + 2
+
+
+# RLBench is highly customizable, in both observations and action modes.
+# This can be a little daunting, so below we have defined some
+# common action modes for you to choose from.
+
+class JointPositionActionMode(ActionMode):
+    """A pre-set, delta joint position action mode or arm and abs for gripper.
+
+    Both the arm and gripper action are applied at the same time.
+    """
+
+    def __init__(self):
+        super(JointPositionActionMode, self).__init__(
+            JointPosition(False), GripperJointPosition(True))
+
+    def action(self, scene: Scene, action: np.ndarray):
+        arm_act_size = np.prod(self.arm_action_mode.action_shape(scene))
+        arm_action = np.array(action[:arm_act_size])
+        ee_action = np.array(action[arm_act_size:])
+        self.arm_action_mode.action_pre_step(scene, arm_action)
+        self.gripper_action_mode.action_pre_step(scene, ee_action)
+        scene.step()
+        self.arm_action_mode.action_post_step(scene, arm_action)
+        self.gripper_action_mode.action_post_step(scene, ee_action)
+
+    def action_shape(self, scene: Scene):
+        return np.prod(self.arm_action_mode.action_shape(scene)) + np.prod(
+            self.gripper_action_mode.action_shape(scene))
+
+    def action_bounds(self):
+        """Returns the min and max of the action mode."""
+        return np.array(7 * [-0.1] + [0.0]), np.array(7 * [0.1] + [0.04])
+
+
+
+class BimanualJointPositionActionMode(ActionMode):
+
+    def __init__(self, arm_action_mode=None, gripper_action_mode=None):
+        arm_action_mode = arm_action_mode or BimanualJointPosition()
+        gripper_action_mode = gripper_action_mode or BimanualDiscrete()
+
+        super(BimanualJointPositionActionMode, self).__init__(arm_action_mode, gripper_action_mode)
+
+    def action(self, scene: Scene, action: np.ndarray):
+
+        assert(action.shape == (16,))
+
+        
+        arm_act_size = np.prod(self.arm_action_mode.action_shape(scene))
+        assert(arm_act_size == 14)
+
+        arm_action = np.concatenate([action[0:7], action[8:15]], axis=0 )
+        ee_action = np.array([action[7], action[15]])
+
+
+        self.arm_action_mode.action_pre_step(scene, arm_action)
+        self.gripper_action_mode.action_pre_step(scene, ee_action)
+
+        self.arm_action_mode.action_step(scene)
+
+        self.arm_action_mode.action_post_step(scene, arm_action)
+        self.gripper_action_mode.action_post_step(scene, ee_action)
+
+    def action_shape(self, scene: Scene):
+        return np.prod(self.arm_action_mode.action_shape(scene)) + np.prod(
+            self.gripper_action_mode.action_shape(scene))
+
+    def action_bounds(self):
+        """Returns the min and max of the action mode."""
+        raise Exception("Not implemented yet.")

external/rlbench/rlbench/action_modes/arm_action_modes.py

@@ -0,0 +1,540 @@
+from abc import abstractmethod
+
+import numpy as np
+from pyquaternion import Quaternion
+
+from pyrep.const import ConfigurationPathAlgorithms as Algos
+from pyrep.const import ConfigurationPathAlgorithms as ObjectType
+from pyrep.errors import ConfigurationPathError, IKError
+from pyrep.const import ObjectType
+from pyrep.objects.dummy import Dummy
+from pyrep.objects.object import Object
+from pyrep.objects.shape import Shape
+from pyrep.robots.arms.arm import Arm
+from pyrep.robots.end_effectors.gripper import Gripper
+
+from rlbench.backend.exceptions import InvalidActionError
+from rlbench.backend.robot import Robot
+from rlbench.backend.robot import UnimanualRobot
+from rlbench.backend.robot import BimanualRobot
+from rlbench.backend.scene import Scene
+from rlbench.const import SUPPORTED_ROBOTS
+
+import logging
+
+from abc import ABC
+
+
+
+def assert_action_shape(action: np.ndarray, expected_shape: tuple):
+    if np.shape(action) != expected_shape:
+        raise InvalidActionError(
+            'Expected the action shape to be: %s, but was shape: %s' % (
+                str(expected_shape), str(np.shape(action))))
+
+
+def assert_unit_quaternion(quat):
+    if not np.isclose(np.linalg.norm(quat), 1.0):
+        raise InvalidActionError('Action contained non unit quaternion!')
+
+
+def calculate_delta_pose(robot: Robot, action: np.ndarray):
+    a_x, a_y, a_z, a_qx, a_qy, a_qz, a_qw = action
+    x, y, z, qx, qy, qz, qw = robot.arm.get_tip().get_pose()
+    new_rot = Quaternion(
+        a_qw, a_qx, a_qy, a_qz) * Quaternion(qw, qx, qy, qz)
+    qw, qx, qy, qz = list(new_rot)
+    pose = [a_x + x, a_y + y, a_z + z] + [qx, qy, qz, qw]
+    return pose
+
+
+class ArmActionMode(ABC):
+    
+    _callable_each_step = None
+
+    def action(self, scene: Scene, action: np.ndarray):
+        self.action_pre_step(scene, action)
+        self.action_step(scene)
+        self.action_post_step(scene, action)    
+
+    def action_step(self, scene: Scene):
+        scene.step()
+        if self._callable_each_step is not None:
+            self._callable_each_step(scene.get_observation())
+
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+        pass
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        pass
+
+    @abstractmethod
+    def action_shape(self, scene: Scene):
+        pass
+
+    def set_control_mode(self, robot: Robot):
+        if isinstance(robot, UnimanualRobot):
+            robot.arm.set_control_loop_enabled(True)
+        elif isinstance(robot, BimanualRobot):
+            logging.info("Setting control mode for both robots")
+            robot.right_arm.set_control_loop_enabled(True)
+            robot.left_arm.set_control_loop_enabled(True)
+
+    def record_end(self, scene, steps=60, step_scene=True):
+        if self._callable_each_step is not None:
+            for _ in range(steps):
+                if step_scene:
+                    scene.step()
+                self._callable_each_step(scene.get_observation())
+
+    def set_callable_each_step(self, callable_each_step):
+        self._callable_each_step = callable_each_step
+
+class JointVelocity(ArmActionMode):
+    """Control the joint velocities of the arm.
+
+    Similar to the action space in many continious control OpenAI Gym envs.
+    """
+
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, self.action_shape(scene))
+        scene.robot.arm.set_joint_target_velocities(action)
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        scene.robot.arm.set_joint_target_velocities(np.zeros_like(action))
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return SUPPORTED_ROBOTS[scene.robot_setup][2],
+
+    def set_control_mode(self, robot: Robot):
+        robot.arm.set_control_loop_enabled(False)
+        robot.arm.set_motor_locked_at_zero_velocity(True)
+
+
+class BimanualJointVelocity(ArmActionMode): 
+
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, self.action_shape(scene))
+        right_action = action[:7]
+        left_action = action[7:]
+        scene.robot.right_arm.set_joint_target_velocities(right_action)
+        scene.robot.left_arm.set_joint_target_velocities(left_action)
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        scene.robot.arm.set_joint_target_velocities(np.zeros_like(action))
+        right_action = action[:7]
+        left_action = action[7:]
+        scene.robot.right_arm.set_joint_target_velocities(np.zeros_like(right_action))
+        scene.robot.left_arm.set_joint_target_velocities(np.zeros_like(left_action))
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return SUPPORTED_ROBOTS[scene.robot_setup][2],
+
+    def set_control_mode(self, robot: Robot):
+        robot.right_arm.set_control_loop_enabled(False)
+        robot.right_arm.set_motor_locked_at_zero_velocity(True)
+        robot.left_arm.set_control_loop_enabled(False)
+        robot.left_arm.set_motor_locked_at_zero_velocity(True)
+        
+
+class BimanualJointPosition(ArmActionMode):
+
+    def __init__(self, absolute_mode: bool = True):
+        self._absolute_mode = absolute_mode
+
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, self.action_shape(scene))
+        
+        right_action = action[:7]
+        left_action = action[7:]
+
+        if not self._absolute_mode:
+            right_action = np.array(scene.robot.right_arm.get_joint_positions()) + right_action
+            left_action = np.array(scene.robot.left_arm.get_joint_positions()) + left_action
+            
+        scene.robot.right_arm.set_joint_target_positions(right_action)
+        scene.robot.left_arm.set_joint_target_positions(left_action)
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        scene.robot.right_arm.set_joint_target_positions(
+            scene.robot.right_arm.get_joint_positions())
+        scene.robot.left_arm.set_joint_target_positions(
+            scene.robot.left_arm.get_joint_positions())
+    
+    def action_shape(self, scene: Scene) -> tuple:
+        return (14, )
+        #return SUPPORTED_ROBOTS[scene.robot_setup][2],
+
+    def unimanual_action_shape(self, scene: Scene) -> tuple:
+        return (7, )
+
+
+
+class JointPosition(ArmActionMode):
+    """Control the target joint positions (absolute or delta) of the arm.
+
+    The action mode opoerates in absolute mode or delta mode, where delta
+    mode takes the current joint positions and adds the new joint positions
+    to get a set of target joint positions. The robot uses a simple control
+    loop to execute until the desired poses have been reached.
+    It os the users responsibility to ensure that the action lies within
+    a usuable range.
+    """
+
+    def __init__(self, absolute_mode: bool = True):
+        """
+        Args:
+            absolute_mode: If we should opperate in 'absolute', or 'delta' mode.
+        """
+        self._absolute_mode = absolute_mode
+
+
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, self.action_shape(scene))
+        if not  self._absolute_mode :
+            action = np.array(scene.robot.arm.get_joint_positions()) + action
+        scene.robot.arm.set_joint_target_positions(action)
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        scene.robot.arm.set_joint_target_positions(
+            scene.robot.arm.get_joint_positions())
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return SUPPORTED_ROBOTS[scene.robot_setup][2],
+
+
+
+class JointTorque(ArmActionMode):
+    """Control the joint torques of the arm.
+    """
+
+    TORQUE_MAX_VEL = 9999
+
+    def _torque_action(self, robot, action):
+        tml = JointTorque.TORQUE_MAX_VEL
+        robot.arm.set_joint_target_velocities(
+            [(tml if t < 0 else -tml) for t in action])
+        robot.arm.set_joint_forces(np.abs(action))
+
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, self.action_shape(scene))
+        self._torque_action(scene.robot, action)
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        self._torque_action(scene.robot, scene.robot.arm.get_joint_forces())
+        scene.robot.arm.set_joint_target_velocities(np.zeros_like(action))
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return SUPPORTED_ROBOTS[scene.robot_setup][2],
+
+
+class EndEffectorPoseViaPlanning(ArmActionMode):
+    """High-level action where target pose is given and reached via planning.
+
+    Given a target pose, a linear path is first planned (via IK). If that fails,
+    sample-based planning will be used. The decision to apply collision
+    checking is a crucial trade off! With collision checking enabled, you
+    are guaranteed collision free paths, but this may not be applicable for task
+    that do require some collision. E.g. using this mode on pushing object will
+    mean that the generated path will actively avoid not pushing the object.
+
+    Note that path planning can be slow, often taking a few seconds in the worst
+    case.
+
+    This was the action mode used in:
+    James, Stephen, and Andrew J. Davison. "Q-attention: Enabling Efficient
+    Learning for Vision-based Robotic Manipulation."
+    arXiv preprint arXiv:2105.14829 (2021).
+    """
+
+    def __init__(self,
+                 absolute_mode: bool = True,
+                 frame: str = 'world',
+                 collision_checking: bool = False):
+        """
+        If collision check is enbled, and an object is grasped, then we
+
+        Args:
+            absolute_mode: If we should opperate in 'absolute', or 'delta' mode.
+            frame: Either 'world' or 'end effector'.
+            collision_checking: IF collision checking is enabled.
+        """
+        self._absolute_mode = absolute_mode
+        self._frame = frame
+        self._collision_checking = collision_checking
+        self._callable_each_step = None
+        self._robot_shapes = None
+
+        if frame not in ['world', 'end effector']:
+            raise ValueError("Expected frame to one of: 'world, 'end effector'")
+
+    def _quick_boundary_check(self, scene: Scene, action: np.ndarray):
+        pos_to_check = action[:3]
+        relative_to = None if self._frame == 'world' else scene.robot.arm.get_tip()
+        if relative_to is not None:
+            scene.target_workspace_check.set_position(pos_to_check, relative_to)
+            pos_to_check = scene.target_workspace_check.get_position()
+        if not scene.check_target_in_workspace(pos_to_check):
+            raise InvalidActionError('A path could not be found because the '
+                                     'target is outside of workspace.')
+
+    def _pose_in_end_effector_frame(self, robot: Robot, action: np.ndarray):
+        a_x, a_y, a_z, a_qx, a_qy, a_qz, a_qw = action
+        x, y, z, qx, qy, qz, qw = robot.arm.get_tip().get_pose()
+        new_rot = Quaternion(
+            a_qw, a_qx, a_qy, a_qz) * Quaternion(qw, qx, qy, qz)
+        qw, qx, qy, qz = list(new_rot)
+        pose = [a_x + x, a_y + y, a_z + z] + [qx, qy, qz, qw]
+        return pose
+    
+    def set_callable_each_step(self, callable_each_step):
+        self._callable_each_step = callable_each_step
+
+
+    def action(self, scene: Scene, action: np.ndarray, ignore_collisions: bool = True):
+        assert_action_shape(action, (7,))
+        assert_unit_quaternion(action[3:])
+        path = self.get_path(scene, action, ignore_collisions, scene.robot.arm, scene.robot.gripper)
+        done = False
+        while not done:
+            done = path.step()
+            scene.step()
+            if self._callable_each_step is not None:
+                self._callable_each_step(scene.get_observation())
+            success, terminate = scene.task.success()
+            # If the task succeeds while traversing path, then break early
+            if success:
+                break    
+
+    def get_path(self, scene: Scene, action: np.ndarray, ignore_collisions: bool, arm: Arm, gripper: Gripper):
+        if not self._absolute_mode and self._frame != 'end effector':
+            action = calculate_delta_pose(scene.robot, action)
+        relative_to = None if self._frame == 'world' else arm.get_tip()
+        self._quick_boundary_check(scene, action)
+
+        colliding_shapes = []
+        if not ignore_collisions:
+            if self._robot_shapes is None:
+                self._robot_shapes = arm.get_objects_in_tree(
+                    object_type=ObjectType.SHAPE)
+            # First check if we are colliding with anything
+            colliding = arm.check_arm_collision()
+            if colliding:
+                # Disable collisions with the objects that we are colliding with
+                grasped_objects = gripper.get_grasped_objects()
+                colliding_shapes = [
+                    s for s in scene.pyrep.get_objects_in_tree(
+                        object_type = ObjectType.SHAPE) if (
+                            s.is_collidable() and
+                            s not in self._robot_shapes and
+                            s not in grasped_objects and
+                            arm.check_arm_collision(
+                                s))]
+                [s.set_collidable(False) for s in colliding_shapes]
+
+        try:
+            # try once with collision checking (if ignore_collisions is true)
+            try:
+                path = arm.get_path(
+                    action[:3],
+                    quaternion=action[3:],
+                    ignore_collisions=ignore_collisions,
+                    relative_to=relative_to,
+                    trials=200, #..TODO was 100
+                    max_configs=10,
+                    max_time_ms=20, #..TODO was 10
+                    trials_per_goal=10, #..TODO was 5
+                    algorithm=Algos.RRTConnect
+                )
+                return path
+            except ConfigurationPathError as e:
+                if ignore_collisions:
+                    raise InvalidActionError(
+                        'A path could not be found. Most likely due to the target '
+                        'being inaccessible or a collison was detected.') from e
+                else:
+                    # try once more with collision checking disabled
+                    path = arm.get_path(
+                        action[:3],
+                        quaternion=action[3:],
+                        ignore_collisions=True,
+                        relative_to=relative_to,
+                        trials=100,
+                        max_configs=10,
+                        max_time_ms=10,
+                        trials_per_goal=5,
+                        algorithm=Algos.RRTConnect
+                    )
+        except ConfigurationPathError as e:
+            raise InvalidActionError(
+                'A path could not be found. Most likely due to the target '
+                'being inaccessible or a collison was detected.') from e
+
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return 7,
+
+
+
+class UnimanualEndEffectorPoseViaPlanning(EndEffectorPoseViaPlanning):
+
+    def __init__(self,
+                 absolute_mode: bool = True,
+                 frame: str = 'world',
+                 collision_checking: bool = False,
+                 robot_name: str = ''):
+        super().__init__(absolute_mode, frame, collision_checking)
+        self.robot_name = robot_name
+
+    def action(self, scene: Scene, action: np.ndarray, ignore_collisions: bool = True):
+        assert_action_shape(action, (7,))
+        assert_unit_quaternion(action[3:])
+        if self.robot_name == 'right':
+            path = self.get_path(scene, action, ignore_collisions, scene.robot.right_arm, scene.robot.right_gripper)
+        elif self.robot_name == 'left':
+            path = self.get_path(scene, action, ignore_collisions, scene.robot.left_arm, scene.robot.left_gripper)
+        else:
+            logging.error('Invalid robot name')
+
+        if not path:
+            logging.warning('No path found')
+            return
+        done = False
+        while not done:
+            done = path.step()
+            scene.step()
+            if self._callable_each_step is not None:
+                # Record observations
+                self._callable_each_step(scene.get_observation())
+            success, terminate = scene.task.success()
+            # If the task succeeds while traversing path, then break early
+            if success and self._callable_each_step is None:
+                break
+
+class BimanualEndEffectorPoseViaPlanning(EndEffectorPoseViaPlanning):
+
+
+    def action(self, scene: Scene, action: np.ndarray, ignore_collisions):
+
+        assert_action_shape(action, self.action_shape(scene))
+ 
+        right_action = action[:7]
+        left_action = action[7:]
+
+        right_ignore_collision = ignore_collisions[0]
+        left_ignore_collison = ignore_collisions[1]
+
+        assert_unit_quaternion(right_action[3:])
+        assert_unit_quaternion(left_action[3:])
+
+        right_done = True
+        left_done = True
+        try:
+            right_path = self.get_path(scene, right_action, right_ignore_collision, scene.robot.right_arm, scene.robot.right_gripper)
+            if right_path:
+                right_done = False
+            else:
+                logging.warning("right path is none")
+        except (ConfigurationPathError, InvalidActionError):
+            pass
+        
+        try:
+            left_path = self.get_path(scene, left_action, left_ignore_collison, scene.robot.left_arm, scene.robot.left_gripper)
+            if left_path:
+                left_done = False
+            else:
+                logging.warning("left path is none")
+        except (ConfigurationPathError, InvalidActionError):
+            pass
+        
+
+        done = False
+
+        while not done:
+            if not right_done and right_path:
+                right_done = right_path.step()
+            if not left_done and left_path:
+                left_done = left_path.step()
+
+            done = right_done and left_done
+            scene.step()
+            if self._callable_each_step is not None:
+                self._callable_each_step(scene.get_observation())
+
+            success, terminate = scene.task.success()
+            # If the task succeeds while traversing path, then break early
+            if success:
+                break
+    
+    def action_shape(self, scene: Scene) -> tuple:
+        return 14,
+
+    def unimanual_action_shape(self, scene: Scene) -> tuple:
+        return 7,
+
+
+class EndEffectorPoseViaIK(ArmActionMode):
+    """High-level action where target pose is given and reached via IK.
+
+    Given a target pose, IK via inverse Jacobian is performed. This requires
+    the target pose to be close to the current pose, otherwise the action
+    will fail. It is up to the user to constrain the action to
+    meaningful values.
+
+    The decision to apply collision checking is a crucial trade off!
+    With collision checking enabled, you are guaranteed collision free paths,
+    but this may not be applicable for task that do require some collision.
+    E.g. using this mode on pushing object will mean that the generated
+    path will actively avoid not pushing the object.
+    """
+
+    def __init__(self,
+                 absolute_mode: bool = True,
+                 frame: str = 'world',
+                 collision_checking: bool = False):
+        """
+        Args:
+            absolute_mode: If we should opperate in 'absolute', or 'delta' mode.
+            frame: Either 'world' or 'end effector'.
+            collision_checking: IF collision checking is enabled.
+        """
+        self._absolute_mode = absolute_mode
+        self._frame = frame
+        self._collision_checking = collision_checking
+        if frame not in ['world', 'end effector']:
+            raise ValueError(
+                "Expected frame to one of: 'world, 'end effector'")
+
+    def action(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, (7,))
+        assert_unit_quaternion(action[3:])
+        if not self._absolute_mode and self._frame != 'end effector':
+            action = calculate_delta_pose(scene.robot, action)
+        relative_to = None if self._frame == 'world' else scene.robot.arm.get_tip()
+
+        try:
+            joint_positions = scene.robot.arm.solve_ik_via_jacobian(
+                action[:3], quaternion=action[3:], relative_to=relative_to)
+            scene.robot.arm.set_joint_target_positions(joint_positions)
+        except IKError as e:
+            raise InvalidActionError(
+                'Could not perform IK via Jacobian; most likely due to current '
+                'end-effector pose being too far from the given target pose. '
+                'Try limiting/bounding your action space.') from e
+        done = False
+        prev_values = None
+        # Move until reached target joint positions or until we stop moving
+        # (e.g. when we collide wth something)
+        while not done:
+            scene.step()
+            cur_positions = scene.robot.arm.get_joint_positions()
+            reached = np.allclose(cur_positions, joint_positions, atol=0.01)
+            not_moving = False
+            if prev_values is not None:
+                not_moving = np.allclose(
+                    cur_positions, prev_values, atol=0.001)
+            prev_values = cur_positions
+            done = reached or not_moving
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return 7,

external/rlbench/rlbench/action_modes/gripper_action_modes.py

@@ -0,0 +1,355 @@
+from abc import abstractmethod
+
+import numpy as np
+
+from rlbench.backend.exceptions import InvalidActionError
+from rlbench.backend.scene import Scene
+
+import logging
+from abc import ABC
+
+
+def assert_action_shape(action: np.ndarray, expected_shape: tuple):
+    if np.shape(action) != expected_shape:
+        raise InvalidActionError(
+            'Expected the action shape to be: %s, but was shape: %s' % (
+                str(expected_shape), str(np.shape(action))))
+
+
+class GripperActionMode(ABC):
+
+    def action(self, scene: Scene, action: np.ndarray):
+        self.action_pre_step(scene, action)
+        self.action_step(scene)
+        self.action_post_step(scene, action)
+
+    def action_step(self, scene: Scene):
+        scene.step()
+
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+        pass
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        pass
+
+    @abstractmethod
+    def action_shape(self, scene: Scene):
+        pass
+
+    @abstractmethod
+    def action_bounds(self):
+        pass
+
+
+class Discrete(GripperActionMode):
+    """Control if the gripper is open or closed in a discrete manner.
+
+    Action values > 0.5 will be discretised to 1 (open), and values < 0.5
+    will be  discretised to 0 (closed).
+    """
+
+    def __init__(self, attach_grasped_objects: bool = True,
+                 detach_before_open: bool = True):
+        self._attach_grasped_objects = attach_grasped_objects
+        self._detach_before_open = detach_before_open
+
+    def _actuate(self, scene, action):
+        done = False
+        while not done:
+            done = scene.robot.gripper.actuate(action, velocity=0.2)
+            scene.pyrep.step()
+            scene.task.step()
+
+    def action(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, self.action_shape(scene.robot))
+        if 0.0 > action[0] > 1.0:
+            raise InvalidActionError(
+                'Gripper action expected to be within 0 and 1.')
+        open_condition = all(
+            x > 0.9 for x in scene.robot.gripper.get_open_amount())
+        current_ee = 1.0 if open_condition else 0.0
+        action = float(action[0] > 0.5)
+
+        if current_ee != action:
+            done = False
+            if not self._detach_before_open:
+                self._actuate(scene, action)
+            if action == 0.0 and self._attach_grasped_objects:
+                # If gripper close action, the check for grasp.
+                for g_obj in scene.task.get_graspable_objects():
+                    scene.robot.gripper.grasp(g_obj)
+            else:
+                # If gripper open action, the check for un-grasp.
+                scene.robot.gripper.release()
+            if self._detach_before_open:
+                self._actuate(scene, action)
+            if action == 1.0:
+                # Step a few more times to allow objects to drop
+                for _ in range(10):
+                    scene.pyrep.step()
+                    scene.task.step()
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return 1,
+
+    def action_bounds(self):
+        """Get the action bounds.
+
+        Returns: Returns the min and max of the action.
+        """
+        return np.array([0]), np.array([1])
+
+
+
+class GripperJointPosition(GripperActionMode):
+    """Control the target joint positions absolute or delta) of the gripper.
+
+    The action mode opoerates in absolute mode or delta mode, where delta
+    mode takes the current joint positions and adds the new joint positions
+    to get a set of target joint positions. The robot uses a simple control
+    loop to execute until the desired poses have been reached.
+    It os the users responsibility to ensure that the action lies within
+    a usuable range.
+
+    Control if the gripper is open or closed in a discrete manner.
+
+    Action values > 0.5 will be discretised to 1 (open), and values < 0.5
+    will be  discretised to 0 (closed).
+    """
+
+    def __init__(self, attach_grasped_objects: bool = True,
+                 detach_before_open: bool = True,
+                 absolute_mode: bool = True):
+        self._attach_grasped_objects = attach_grasped_objects
+        self._detach_before_open = detach_before_open
+        self._absolute_mode = absolute_mode
+        self._control_mode_set = False
+
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+        if not self._control_mode_set:
+            scene.robot.gripper.set_control_loop_enabled(True)
+            self._control_mode_set = True
+        assert_action_shape(action, self.action_shape(scene.robot))
+        action = action.repeat(2)  # use same action for both joints
+        a = action if self._absolute_mode else np.array(
+            scene.robot.gripper.get_joint_positions())
+        scene.robot.gripper.set_joint_target_positions(a)
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        scene.robot.gripper.set_joint_target_positions(
+            scene.robot.gripper.get_joint_positions())
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return 1,
+
+    def action_bounds(self):
+        """Get the action bounds.
+
+        Returns: Returns the min and max of the action.
+        """
+        return np.array([0]), np.array([0.04])
+    
+
+class BimanualGripperJointPosition(GripperJointPosition):
+    
+    def action_pre_step(self, scene: Scene, action: np.ndarray):
+
+        if not self._control_mode_set:
+            scene.robot.right_gripper.set_control_loop_enabled(True)
+            scene.robot.left_gripper.set_control_loop_enabled(True)
+            self._control_mode_set = True
+
+        assert_action_shape(action, self.action_shape(scene.robot))
+
+        right_action = action[:1].repeat(2) 
+        left_action = action[1:].repeat(2) 
+
+        if not self._absolute_mode:
+            right_action = right_action + np.array(scene.robot.gripper.get_joint_positions())
+            left_action = left_action + np.array(scene.robot.gripper.get_joint_positions())
+            
+        scene.robot.right_gripper.set_joint_target_positions(right_action)
+        scene.robot.left_gripper.set_joint_target_positions(left_action)
+
+    def action_post_step(self, scene: Scene, action: np.ndarray):
+        scene.robot.right_gripper.set_joint_target_positions(
+            scene.robot.right_gripper.get_joint_positions())
+        
+        scene.robot.left_gripper.set_joint_target_positions(
+            scene.robot.left_gripper.get_joint_positions())        
+    
+    def action_shape(self, scene: Scene) -> tuple:
+        return 2,
+
+
+
+class UnimanualDiscrete(GripperActionMode):
+    """Control if the gripper is open or closed in a discrete manner.
+
+    Action values > 0.5 will be discretised to 1 (open), and values < 0.5
+    will be  discretised to 0 (closed).
+    """
+
+    def __init__(self, attach_grasped_objects: bool = True,
+                 detach_before_open: bool = True,
+                 robot_name: str = 'left'):
+        self._attach_grasped_objects = attach_grasped_objects
+        self._detach_before_open = detach_before_open
+        self.robot_name = robot_name
+
+
+    def _actuate(self, scene, action):
+        done = False
+        while not done:
+            if self.robot_name == 'right':
+                done = scene.robot.right_gripper.actuate(action, velocity=0.2)
+            elif self.robot_name == 'left':
+                done = scene.robot.left_gripper.actuate(action, velocity=0.2)
+            else:
+                done = scene.robot.gripper.actuate(action, velocity=0.2)
+
+            scene.pyrep.step()
+            scene.task.step()
+
+    def action(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, self.action_shape(scene.robot))
+        if 0.0 > action[0] > 1.0:
+            raise InvalidActionError(
+                'Gripper action expected to be within 0 and 1.')
+        if self.robot_name == 'right':
+            open_condition = all(x > 0.9 for x in scene.robot.right_gripper.get_open_amount())
+        else:
+            open_condition = all(x > 0.9 for x in scene.robot.left_gripper.get_open_amount())
+        current_ee = 1.0 if open_condition else 0.0
+        action = float(action[0] > 0.5)
+
+        if current_ee != action:
+            done = False
+            if not self._detach_before_open:
+                self._actuate(scene, action)
+            if action == 0.0 and self._attach_grasped_objects:
+                # If gripper close action, the check for grasp.
+                for g_obj in scene.task.get_graspable_objects():
+                    if self.robot_name == 'right':
+                        scene.robot.right_gripper.grasp(g_obj)
+                    else:
+                        scene.robot.left_gripper.grasp(g_obj)
+            else:
+                # If gripper open action, the check for un-grasp.
+                if self.robot_name == 'right':
+                    scene.robot.right_gripper.release()
+                else:
+                    scene.robot.left_gripper.release()
+            if self._detach_before_open:
+                self._actuate(scene, action)
+            if action == 1.0:
+                # Step a few more times to allow objects to drop
+                for _ in range(10):
+                    scene.pyrep.step()
+                    scene.task.step()
+
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return 1,
+
+    def action_bounds(self):
+        """Get the action bounds.
+
+        Returns: Returns the min and max of the action.
+        """
+        return np.array([0]), np.array([0.04])
+
+
+
+class BimanualDiscrete(Discrete):
+    
+    def _actuate(self, scene, action):
+
+        right_action = action[0]
+        left_action = action[1]
+        done = False
+        right_done = False
+        left_done = False
+
+        while not done:
+            if not right_done:
+                right_done = scene.robot.right_gripper.actuate(right_action, velocity=0.2)
+            if not left_done:
+                left_done = scene.robot.left_gripper.actuate(left_action, velocity=0.2)
+            done = right_done and left_done
+            scene.pyrep.step()
+            scene.task.step()
+
+    def action(self, scene: Scene, action: np.ndarray):
+        assert_action_shape(action, self.action_shape(scene.robot))
+        if 0.0 > action[0] > 1.0:
+            raise InvalidActionError(
+                'Gripper action expected to be within 0 and 1.')
+
+        if 0.0 > action[1] > 1.0:
+            raise InvalidActionError(
+                'Gripper action expected to be within 0 and 1.')
+
+        right_open_condition = all(
+            x > 0.9 for x in scene.robot.right_gripper.get_open_amount())
+
+        left_open_condition = all(
+            x > 0.9 for x in scene.robot.left_gripper.get_open_amount())
+
+        right_current_ee = 1.0 if right_open_condition else 0.0
+        left_current_ee = 1.0 if left_open_condition else 0.0
+
+        right_action = float(action[0] > 0.5)
+        left_action = float(action[1] > 0.5)
+
+        if right_current_ee != right_action or left_current_ee != left_action:
+            if not self._detach_before_open:
+                self._actuate(scene, action)
+
+
+        if right_current_ee != right_action:
+            if right_action == 0.0 and self._attach_grasped_objects:
+                # If gripper close action, the check for grasp.
+                left_grasped_objects = scene.robot.left_gripper.get_grasped_objects()
+                for g_obj in scene.task.get_graspable_objects():
+                    if g_obj in left_grasped_objects:
+                        logging.warning("Object with name %s is already grasped by left robot", g_obj.get_name())
+                    else:
+                        scene.robot.right_gripper.grasp(g_obj)
+            else:
+                # If gripper open action, the check for un-grasp.
+                scene.robot.right_gripper.release()
+        if left_current_ee != left_action:
+            if left_action == 0.0 and self._attach_grasped_objects:
+                right_grasped_objects = scene.robot.right_gripper.get_grasped_objects()
+                # If gripper close action, the check for grasp.                
+                for g_obj in scene.task.get_graspable_objects():
+                    if g_obj in right_grasped_objects:
+                        logging.warning("Object with name %s is already grasped by right robot", g_obj.get_name())
+                    else:
+                        scene.robot.left_gripper.grasp(g_obj)
+            else:
+                # If gripper open action, the check for un-grasp.
+                scene.robot.left_gripper.release()
+
+        if right_current_ee != right_action or left_current_ee != left_action:
+            if self._detach_before_open:
+                self._actuate(scene, action)
+            if right_action == 1.0 or left_action == 1.0:
+                # Step a few more times to allow objects to drop
+                for _ in range(10):
+                    scene.pyrep.step()
+                    scene.task.step()
+
+    def action_shape(self, scene: Scene) -> tuple:
+        return 2,
+
+    def unimanual_action_shape(self, scene: Scene) -> tuple:
+        return 1,
+
+    def action_bounds(self):
+        """Get the action bounds.
+
+        Returns: Returns the min and max of the action.
+        """
+        return np.array([0]), np.array([0.04])

external/rlbench/rlbench/assets/procedural_objects/000/000.mtl

@@ -0,0 +1,12 @@
+# Blender MTL File: 'None'
+# Material Count: 1
+
+newmtl 000_mtl
+Ns 96.078431
+Ka 0.000000 0.000000 0.000000
+Kd 0.300113 0.644390 0.268083
+Ks 0.500000 0.500000 0.500000
+Ni 1.000000
+d 1.000000
+illum 2
+map_Kd 000.png

external/rlbench/rlbench/assets/procedural_objects/000/000.urdf

@@ -0,0 +1,32 @@
+
+<robot name="blob000">
+  <link name="random_obj_000">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0"/>
+      <inertia_scaling value="3.0"/>
+      <contact_cfm value="0.0"/>
+      <contact_erp value="1.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <mass value="0.0914"/>
+      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="0"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="000.obj" scale="0.0076 0.0076 0.0076"/>
+      </geometry>
+      <material name="blockmat">
+        <color rgba="0.54 0.51 1.00 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="000_coll.obj" scale="0.0076 0.0076 0.0076"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>

external/rlbench/rlbench/assets/procedural_objects/000/000_coll.mtl

@@ -0,0 +1,10 @@
+# Blender MTL File: 'None'
+# Material Count: 1
+
+newmtl None
+Ns 0
+Ka 0.000000 0.000000 0.000000
+Kd 0.8 0.8 0.8
+Ks 0.8 0.8 0.8
+d 1
+illum 2

external/rlbench/rlbench/assets/procedural_objects/000/000_coll.obj

@@ -0,0 +1,612 @@
+o convex_0
+v 1.838294 -1.840837 -1.255292
+v -0.026924 -1.393016 -1.255292
+v -0.026924 -1.393016 -1.217907
+v 0.196603 0.062330 -1.255292
+v 1.801152 -2.773957 -2.822649
+v 1.577172 -2.325860 -1.217907
+v 1.726643 -2.213696 -2.785264
+v 1.577172 -1.132320 -1.217907
+v 2.286481 -2.661794 -2.300040
+v 0.905685 -2.139013 -1.292677
+v 1.427928 -2.288379 -2.673265
+v 0.122094 0.062330 -1.255292
+v 2.249113 -2.773957 -2.225426
+v 0.122094 0.062330 -1.217907
+v 1.614540 -1.429941 -1.703288
+v 2.062500 -2.736477 -2.785106
+v 1.838294 -1.691193 -1.217907
+v 1.763785 -2.736199 -2.598651
+v 1.689049 -2.325860 -1.255292
+v 1.726643 -2.102088 -2.635879
+v 0.271338 -1.653990 -1.217907
+v 1.763785 -1.429941 -1.330063
+v 1.390560 -2.363340 -2.635879
+v 2.286481 -2.736477 -2.225426
+v 0.681931 -1.952722 -1.852358
+v 1.651908 -2.251177 -2.785264
+v 1.539805 -1.094839 -1.255292
+v 1.913256 -2.363062 -2.673265
+v 0.047585 -1.393016 -1.330063
+v 0.868543 -2.102088 -1.217907
+v 1.577172 -2.624314 -2.524037
+v 1.241315 -2.064886 -2.486495
+v 2.286481 -2.773957 -2.412038
+v 2.137010 -2.736477 -2.150655
+f 18 13 34 
+f 3 6 8 
+f 2 3 12 
+f 4 7 12 
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external/rlbench/rlbench/assets/procedural_objects/000/test.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+
+import sys
+import os
+import subprocess
+
+# Script taken from doing the needed operation
+# (Filters > Remeshing, Simplification and Reconstruction >
+# Quadric Edge Collapse Decimation, with parameters:
+# 0.9 percentage reduction (10%), 0.3 Quality threshold (70%)
+# Target number of faces is ignored with those parameters
+# conserving face normals, planar simplification and
+# post-simplimfication cleaning)
+# And going to Filter > Show current filter script
+filter_script_mlx = """<!DOCTYPE FilterScript>
+<FilterScript>
+ <filter name="Quadric Edge Collapse Decimation">
+  <Param type="RichInt" value="64" name="TargetFaceNum"/>
+  <Param type="RichFloat" value="0.0" name="TargetPerc"/>
+  <Param type="RichFloat" value="0.3" name="QualityThr"/>
+  <Param type="RichBool" value="false" name="PreserveBoundary"/>
+  <Param type="RichFloat" value="1" name="BoundaryWeight"/>
+  <Param type="RichBool" value="true" name="PreserveNormal"/>
+  <Param type="RichBool" value="false" name="PreserveTopology"/>
+  <Param type="RichBool" value="false" name="OptimalPlacement"/>
+  <Param type="RichBool" value="true" name="PlanarQuadric"/>
+  <Param type="RichBool" value="false" name="QualityWeight"/>
+  <Param type="RichBool" value="true" name="AutoClean"/>
+  <Param type="RichBool" value="false" name="Selected"/>
+ </filter>
+</FilterScript>
+
+"""
+
+
+
+def create_tmp_filter_file(filename='filter_file_tmp.mlx'):
+    with open('/tmp/' + filename, 'w') as f:
+        f.write(filter_script_mlx)
+    return '/tmp/' + filename
+
+
+def reduce_faces(in_file, out_file,
+                 filter_script_path=create_tmp_filter_file()):
+    # Add input mesh
+    command = "meshlabserver -i " + in_file
+    # Add the filter script
+    command += " -s " + filter_script_path
+    # Add the output filename and output flags
+    command += " -o " + out_file + " -om vn fn"
+    # Execute command
+    print ("Going to execute: " + command)
+    output = subprocess.check_output(command, shell=True)
+    last_line = output.splitlines()[-1]
+    print ("Done:")
+    print (in_file + " > " + out_file + ": " + last_line)
+
+
+if __name__ == '__main__':
+    if len(sys.argv) < 3:
+        print ("Usage:")
+        print (sys.argv[0] + " /path/to/input_mesh num_iterations")
+        print ("For example, reduce 10 times:")
+        print (sys.argv[0] + " /home/myuser/mymesh.dae 10")
+        exit(0)
+
+    in_mesh = sys.argv[1]
+    filename = in_mesh.split('/')[-1]
+    num_iterations = int(sys.argv[2])
+
+    folder_name = filename.replace('.', '_')
+    tmp_folder_name = '/tmp/' + folder_name + '_meshes/'
+
+    print ("Input mesh: " + in_mesh + " (filename: " + filename + ")")
+    print ("Num iterations: " + str(num_iterations))
+    print ("Output folder: " + tmp_folder_name)
+    try:
+        os.mkdir(tmp_folder_name)
+    except OSError as e:
+        print >> sys.stderr, "Exception creating folder for meshes: " + str(e)
+        exit(0)
+    for it in range(0, num_iterations):
+        if it == 0:
+            out_mesh = tmp_folder_name + folder_name + "_it" + str(it) + ".obj"
+            reduce_faces(in_mesh, out_mesh)
+        else:
+            out_mesh = tmp_folder_name + folder_name + "_it" + str(it) + ".obj"
+            reduce_faces(last_out_mesh, out_mesh)
+        last_out_mesh = out_mesh
+
+    print ("Done reducing, find the files at: " + tmp_folder_name)

external/rlbench/rlbench/assets/procedural_objects/001/001.mtl

@@ -0,0 +1,12 @@
+# Blender MTL File: 'None'
+# Material Count: 1
+
+newmtl 001_mtl
+Ns 96.078431
+Ka 0.000000 0.000000 0.000000
+Kd 0.575108 0.577684 0.119316
+Ks 0.500000 0.500000 0.500000
+Ni 1.000000
+d 1.000000
+illum 2
+map_Kd 001.png

external/rlbench/rlbench/assets/procedural_objects/001/001.obj

@@ -0,0 +1,527 @@
+# Blender v2.69 (sub 0) OBJ File: ''
+# www.blender.org
+mtllib 001.mtl
+o 001_Cube.017
+v -0.828813 0.484638 0.056215
+v -0.860224 5.907092 0.572731
+v -0.827642 0.359975 -0.039965
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+v -0.819609 0.219728 -0.082243
+v -0.843937 0.626271 0.118786
+v -0.807566 5.998513 -0.580538
+v -1.136643 0.754140 -0.021758
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+v -0.233287 0.540319 0.568062
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external/rlbench/rlbench/assets/procedural_objects/001/001.urdf

@@ -0,0 +1,32 @@
+
+<robot name="blob001">
+  <link name="random_obj_001">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0"/>
+      <inertia_scaling value="3.0"/>
+      <contact_cfm value="0.0"/>
+      <contact_erp value="1.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <mass value="0.1317"/>
+      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="0"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="001.obj" scale="0.0090 0.0090 0.0090"/>
+      </geometry>
+      <material name="blockmat">
+        <color rgba="0.70 0.13 0.56 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="001_coll.obj" scale="0.0090 0.0090 0.0090"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>

external/rlbench/rlbench/assets/procedural_objects/001/001_coll.mtl

@@ -0,0 +1,10 @@
+# Blender MTL File: 'None'
+# Material Count: 1
+
+newmtl None
+Ns 0
+Ka 0.000000 0.000000 0.000000
+Kd 0.8 0.8 0.8
+Ks 0.8 0.8 0.8
+d 1
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external/rlbench/rlbench/assets/procedural_objects/001/001_coll.obj

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external/rlbench/rlbench/assets/procedural_objects/002/002.mtl

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external/rlbench/rlbench/assets/procedural_objects/002/002.obj

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external/rlbench/rlbench/assets/procedural_objects/002/002.urdf

@@ -0,0 +1,32 @@
+
+<robot name="blob002">
+  <link name="random_obj_002">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0"/>
+      <inertia_scaling value="3.0"/>
+      <contact_cfm value="0.0"/>
+      <contact_erp value="1.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <mass value="0.0945"/>
+      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="0"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="002.obj" scale="0.0071 0.0071 0.0071"/>
+      </geometry>
+      <material name="blockmat">
+        <color rgba="0.11 0.63 0.41 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="002_coll.obj" scale="0.0071 0.0071 0.0071"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>

external/rlbench/rlbench/assets/procedural_objects/002/002_coll.mtl

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+# Blender MTL File: 'None'
+# Material Count: 1
+
+newmtl None
+Ns 0
+Ka 0.000000 0.000000 0.000000
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+Ks 0.8 0.8 0.8
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external/rlbench/rlbench/assets/procedural_objects/003/003.mtl

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+# Material Count: 1
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external/rlbench/rlbench/assets/procedural_objects/003/003.obj

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+# www.blender.org
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external/rlbench/rlbench/assets/procedural_objects/003/003.urdf

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+  <link name="random_obj_003">
+    <contact>
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+      <rolling_friction value="0.0"/>
+      <inertia_scaling value="3.0"/>
+      <contact_cfm value="0.0"/>
+      <contact_erp value="1.0"/>
+    </contact>
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+      <mass value="0.0593"/>
+      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="0"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="003.obj" scale="0.0064 0.0064 0.0064"/>
+      </geometry>
+      <material name="blockmat">
+        <color rgba="0.96 0.84 0.49 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="003_coll.obj" scale="0.0064 0.0064 0.0064"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>

external/rlbench/rlbench/assets/procedural_objects/003/003_coll.mtl

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+# Material Count: 1
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external/rlbench/rlbench/assets/procedural_objects/003/003_coll.obj

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external/rlbench/rlbench/assets/procedural_objects/004/004.mtl

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+# Blender MTL File: 'None'
+# Material Count: 1
+
+newmtl 004_mtl
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+map_Kd 004.png

external/rlbench/rlbench/assets/procedural_objects/004/004.urdf

@@ -0,0 +1,32 @@
+
+<robot name="blob004">
+  <link name="random_obj_004">
+    <contact>
+      <lateral_friction value="1.0"/>
+      <rolling_friction value="0.0"/>
+      <inertia_scaling value="3.0"/>
+      <contact_cfm value="0.0"/>
+      <contact_erp value="1.0"/>
+    </contact>
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <mass value="0.0873"/>
+      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="0"/>
+    </inertial>
+    <visual>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="004.obj" scale="0.0059 0.0059 0.0059"/>
+      </geometry>
+      <material name="blockmat">
+        <color rgba="0.89 0.15 0.78 1"/>
+      </material>
+    </visual>
+    <collision>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+      <geometry>
+        <mesh filename="004_coll.obj" scale="0.0059 0.0059 0.0059"/>
+      </geometry>
+    </collision>
+  </link>
+</robot>

external/rlbench/rlbench/assets/procedural_objects/004/004_coll.mtl

@@ -0,0 +1,10 @@
+# Blender MTL File: 'None'
+# Material Count: 1
+
+newmtl None
+Ns 0
+Ka 0.000000 0.000000 0.000000
+Kd 0.8 0.8 0.8
+Ks 0.8 0.8 0.8
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