Merge pull request #316 from StanfordVL/ig-develop

Release v2.2.2

.github/workflows/docs.yml

@@ -6,7 +6,7 @@ jobs:
   build-docs:
     name: Build and deploy documentation
     runs-on: ubuntu-latest
-    if: github.repository == 'StanfordVL/iGibson' && github.ref == 'refs/heads/master'
+    if: github.ref == 'refs/heads/master'
 
     steps:
     - name: Checkout code

.github/workflows/examples-as-test.yml

@@ -15,7 +15,6 @@ concurrency:
 jobs:
   test:
     runs-on: [self-hosted, linux, gpu]
-    if: github.repository == 'StanfordVL/iGibson-dev'
 
     steps:
       - name: Checkout source
@@ -63,9 +62,8 @@ jobs:
       - name: Checkout BDDL
         uses: actions/checkout@v2
         with:
-          repository: StanfordVL/bddl-dev
-          ref: 581be50e7cfd2b3a1447aaa1b4fc2424b673339c
-          token: ${{ secrets.PERSONAL_ACCESS_TOKEN }}  # PAT is required since this is a different repo
+          repository: StanfordVL/bddl
+          ref: 7fba5f13cc9f3c83dcce9ef4b014903bbcd6e34f
           path: bddl
           submodules: recursive
           lfs: true

.github/workflows/exhaustive-examples-as-test.yml

@@ -15,7 +15,6 @@ concurrency:
 jobs:
   test:
     runs-on: [self-hosted, linux, gpu]
-    if: github.repository == 'StanfordVL/iGibson-dev'
 
     steps:
       - name: Checkout source
@@ -63,9 +62,8 @@ jobs:
       - name: Checkout BDDL
         uses: actions/checkout@v2
         with:
-          repository: StanfordVL/bddl-dev
-          ref: 581be50e7cfd2b3a1447aaa1b4fc2424b673339c
-          token: ${{ secrets.PERSONAL_ACCESS_TOKEN }}  # PAT is required since this is a different repo
+          repository: StanfordVL/bddl
+          ref: 7fba5f13cc9f3c83dcce9ef4b014903bbcd6e34f
           path: bddl
           submodules: recursive
           lfs: true

.github/workflows/release.yml

@@ -31,7 +31,7 @@ jobs:
         --outdir dist/
         .
     - name: Publish a Python distribution to PyPI
-      if: github.repository == 'StanfordVL/iGibson' && startsWith(github.ref, 'refs/tags')
+      if: startsWith(github.ref, 'refs/tags')
       uses: pypa/gh-action-pypi-publish@release/v1
       with:
         user: __token__

.github/workflows/tests.yml

@@ -9,7 +9,6 @@ concurrency:
 jobs:
   test:
     runs-on: [self-hosted, linux, gpu]
-    if: github.repository == 'StanfordVL/iGibson-dev'
 
     steps:
       - name: Checkout source
@@ -51,9 +50,8 @@ jobs:
       - name: Checkout BDDL
         uses: actions/checkout@v2
         with:
-          repository: StanfordVL/bddl-dev
-          ref: 581be50e7cfd2b3a1447aaa1b4fc2424b673339c
-          token: ${{ secrets.PERSONAL_ACCESS_TOKEN }}  # PAT is required since this is a different repo
+          repository: StanfordVL/bddl
+          ref: 7fba5f13cc9f3c83dcce9ef4b014903bbcd6e34f
           path: bddl
           submodules: recursive
           lfs: true

README.md

@@ -27,14 +27,20 @@ iGibson is a simulation environment providing fast visual rendering and physics
 If you use iGibson or its assets and models, consider citing the following publication:
 
 ```
-@misc{li2021igibson,
-      title={iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks}, 
-      author={Chengshu Li and Fei Xia and Roberto Mart\'in-Mart\'in and Michael Lingelbach and Sanjana Srivastava and Bokui Shen and Kent Vainio and Cem Gokmen and Gokul Dharan and Tanish Jain and Andrey Kurenkov and Karen Liu and Hyowon Gweon and Jiajun Wu and Li Fei-Fei and Silvio Savarese},
-      year={2021},
-      eprint={2108.03272},
-      archivePrefix={arXiv},
-      primaryClass={cs.RO}
-}
+@inproceedings{li2022igibson,
+  title = 	 {iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks},
+  author =       {Li, Chengshu and Xia, Fei and Mart\'in-Mart\'in, Roberto and Lingelbach, Michael and Srivastava, Sanjana and Shen, Bokui and Vainio, Kent Elliott and Gokmen, Cem and Dharan, Gokul and Jain, Tanish and Kurenkov, Andrey and Liu, Karen and Gweon, Hyowon and Wu, Jiajun and Fei-Fei, Li and Savarese, Silvio},
+  booktitle = 	 {Proceedings of the 5th Conference on Robot Learning},
+  pages = 	 {455--465},
+  year = 	 {2022},
+  editor = 	 {Faust, Aleksandra and Hsu, David and Neumann, Gerhard},
+  volume = 	 {164},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {08--11 Nov},
+  publisher =    {PMLR},
+  pdf = 	 {https://proceedings.mlr.press/v164/li22b/li22b.pdf},
+  url = 	 {https://proceedings.mlr.press/v164/li22b.html},
+} 
 ```
 
 ```

docker/.env

@@ -1 +1 @@
-VERSION=v2.2.1
+VERSION=v2.2.2

docs/intro.md

@@ -12,6 +12,23 @@ iGibson implements all features required to evaluate AI solutions in the BEHAVIO
 ### Citation
 If you use iGibson or its assets and models, consider citing the following publications:
 
+```
+@InProceedings{li2022igibson,
+  title = 	 {iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks},
+  author =       {Li, Chengshu and Xia, Fei and Mart\'in-Mart\'in, Roberto and Lingelbach, Michael and Srivastava, Sanjana and Shen, Bokui and Vainio, Kent Elliott and Gokmen, Cem and Dharan, Gokul and Jain, Tanish and Kurenkov, Andrey and Liu, Karen and Gweon, Hyowon and Wu, Jiajun and Fei-Fei, Li and Savarese, Silvio},
+  booktitle = 	 {Proceedings of the 5th Conference on Robot Learning},
+  pages = 	 {455--465},
+  year = 	 {2022},
+  editor = 	 {Faust, Aleksandra and Hsu, David and Neumann, Gerhard},
+  volume = 	 {164},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {08--11 Nov},
+  publisher =    {PMLR},
+  pdf = 	 {https://proceedings.mlr.press/v164/li22b/li22b.pdf},
+  url = 	 {https://proceedings.mlr.press/v164/li22b.html},
+} 
+```
+
 ```
 @inproceedings{shen2021igibson,
       title={iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes}, 
@@ -23,17 +40,6 @@ If you use iGibson or its assets and models, consider citing the following publi
 }
 ```
 
-```
-@misc{li2021igibson,
-      title={iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks}, 
-      author={Chengshu Li and Fei Xia and Roberto Martín-Martín and Michael Lingelbach and Sanjana Srivastava and Bokui Shen and Kent Vainio and Cem Gokmen and Gokul Dharan and Tanish Jain and Andrey Kurenkov and Karen Liu and Hyowon Gweon and Jiajun Wu and Li Fei-Fei and Silvio Savarese},
-      year={2021},
-      eprint={2108.03272},
-      archivePrefix={arXiv},
-      primaryClass={cs.RO}
-}
-```
-
 ### Code Release
 The GitHub repository of iGibson can be found [here](https://github.com/StanfordVL/iGibson). Bug reports, suggestions for improvement, as well as community developments are encouraged and appreciated. 
 

igibson/__init__.py

@@ -4,7 +4,7 @@
 import packaging.version
 import yaml
 
-__version__ = "2.2.1"
+__version__ = "2.2.2"
 
 __logo__ = r"""
  _   _____  _  _

igibson/controllers/null_gripper_controller.py

@@ -48,7 +48,7 @@ def __init__(
         super().__init__(
             control_freq=control_freq,
             control_limits=control_limits,
-            joint_idx=np.array([], dtype=np.int),  # no joints controlled
+            joint_idx=np.array([], dtype=int),  # no joints controlled
             command_input_limits=command_input_limits,
             command_output_limits=command_output_limits,
         )

igibson/examples/observations/generate_lidar_colored_pointcloud.py

@@ -36,8 +36,8 @@ def get_lidar_sampling_pattern():
 
     height = 128
 
-    x_samples = (np.tan(xx) / np.cos(yy) * height // 2 + height // 2).astype(np.int)
-    y_samples = (np.tan(yy) * height // 2 + height // 2).astype(np.int)
+    x_samples = (np.tan(xx) / np.cos(yy) * height // 2 + height // 2).astype(int)
+    y_samples = (np.tan(yy) * height // 2 + height // 2).astype(int)
 
     x_samples = x_samples.flatten()
     y_samples = y_samples.flatten()

igibson/examples/scenes/scenescan2igibson/generate_floor_map.py

@@ -109,7 +109,7 @@ def gen_map(obj_filepath, mesh_dir, img_filename_format="floor_{}.png"):
     xmax, ymax, _ = vertices.max(axis=0)
 
     max_length = np.max([np.abs(xmin), np.abs(ymin), np.abs(xmax), np.abs(ymax)])
-    max_length = np.ceil(max_length).astype(np.int)
+    max_length = np.ceil(max_length).astype(int)
 
     with open(os.path.join(mesh_dir, "floors.txt")) as f:
         floors = map(float, f.readlines())

igibson/examples/scenes/scenescan2igibson/generate_traversable_map.py

@@ -112,7 +112,7 @@ def gen_trav_map(mp3d_dir, add_clutter=False):
             xmax, ymax, _ = vertices.max(axis=0)
 
             max_length = np.max([np.abs(xmin), np.abs(ymin), np.abs(xmax), np.abs(ymax)])
-            max_length = np.ceil(max_length).astype(np.int)
+            max_length = np.ceil(max_length).astype(int)
 
             for i_floor in range(len(floors)):
                 floor = floors[i_floor]

igibson/external/pybullet_tools/utils.py

@@ -3419,7 +3419,7 @@ def collision_fn(q):
                              robot_footprint_radius_in_map * 2 + 1))
             cv2.circle(mask, (robot_footprint_radius_in_map, robot_footprint_radius_in_map), robot_footprint_radius_in_map,
                        1, -1)
-            mask = mask.astype(np.bool)
+            mask = mask.astype(bool)
 
             # Check if all the points where the shifted mask of the robot base overlaps with the occupancy map are
             # marked as FREESPACE, and return false if not

igibson/object_states/adjacency.py

@@ -70,7 +70,7 @@ def compute_adjacencies(obj, axes, max_distance):
     directions[1::2] = -axes
 
     # For now, we keep our result in the dimensionality of (direction, hit_object_order).
-    finalized = np.zeros(directions.shape[0], dtype=np.bool)
+    finalized = np.zeros(directions.shape[0], dtype=bool)
     bodies_by_direction = [[] for _ in directions]
 
     # Prepare this object's info for ray casting.
@@ -104,7 +104,7 @@ def compute_adjacencies(obj, axes, max_distance):
         )
 
         # Get the object IDs per axis and filter out self-hit cases.
-        obj_ids = np.array([result[0] for result in ray_results], dtype=np.int)
+        obj_ids = np.array([result[0] for result in ray_results], dtype=int)
 
         # Add the results to the appropriate lists
         for direction_idx, result in enumerate(obj_ids):

igibson/object_states/utils.py

@@ -163,7 +163,7 @@ def sample_kinematics(
                 height, height_map = objB.supporting_surfaces[predicate][(body_id, link_id)][random_height_idx]
                 obj_half_size = np.max(objA.bounding_box) / 2 * 100
                 obj_half_size_scaled = np.array([obj_half_size / objB.scale[1], obj_half_size / objB.scale[0]])
-                obj_half_size_scaled = np.ceil(obj_half_size_scaled).astype(np.int)
+                obj_half_size_scaled = np.ceil(obj_half_size_scaled).astype(int)
                 height_map_eroded = cv2.erode(height_map, np.ones(obj_half_size_scaled, np.uint8))
 
                 valid_pos = np.array(height_map_eroded.nonzero())

igibson/render/mesh_renderer/mesh_renderer_cpu.py

@@ -1764,8 +1764,8 @@ def setup_lidar_param(self):
         xx = xx.flatten()
         yy = yy.flatten()
 
-        x_samples = (np.tan(xx) / np.cos(yy) * self.height // 2 + self.height // 2).astype(np.int)
-        y_samples = (np.tan(yy) * self.height // 2 + self.height // 2).astype(np.int)
+        x_samples = (np.tan(xx) / np.cos(yy) * self.height // 2 + self.height // 2).astype(int)
+        y_samples = (np.tan(yy) * self.height // 2 + self.height // 2).astype(int)
 
         self.x_samples = x_samples.flatten()
         self.y_samples = y_samples.flatten()

igibson/robots/jr2.py

@@ -138,7 +138,7 @@ def gripper_control_idx(self):
         :return dict[str, Array[int]]: Dictionary mapping arm appendage name to indices in low-level control
             vector corresponding to gripper joints.
         """
-        return {self.default_arm: np.array([8, 9], dtype=np.int)}
+        return {self.default_arm: np.array([8, 9], dtype=int)}
 
     @property
     def disabled_collision_pairs(self):

igibson/scenes/igibson_indoor_scene.py

@@ -1042,7 +1042,7 @@ def world_to_seg_map(self, xy):
         :param xy: 2D location in world reference frame (metric)
         :return: 2D location in seg map reference frame (image)
         """
-        return np.flip((xy / self.seg_map_resolution + self.seg_map_size / 2.0)).astype(np.int)
+        return np.flip((xy / self.seg_map_resolution + self.seg_map_size / 2.0)).astype(int)
 
     def get_room_type_by_point(self, xy):
         """

igibson/scenes/indoor_scene.py

@@ -176,7 +176,7 @@ def world_to_map(self, xy):
         :param xy: 2D location in world reference frame (metric)
         :return: 2D location in map reference frame (image)
         """
-        return np.flip((np.array(xy) / self.trav_map_resolution + self.trav_map_size / 2.0)).astype(np.int)
+        return np.flip((np.array(xy) / self.trav_map_resolution + self.trav_map_size / 2.0)).astype(int)
 
     def has_node(self, floor, world_xy):
         """

igibson/utils/data_utils/ext_scene/scripts/step_1_preprocess_3dfront.py

@@ -418,7 +418,7 @@ def get_scene_range(model_id):
     xmin, ymin = stacked.min(axis=0)
     xmax, ymax = stacked.max(axis=0)
     max_length = np.max([np.abs(xmin), np.abs(ymin), np.abs(xmax), np.abs(ymax)])
-    max_length = np.ceil(max_length).astype(np.int)
+    max_length = np.ceil(max_length).astype(int)
     return max_length
 
 

igibson/utils/data_utils/ext_scene/scripts/step_1_preprocess_cubicasa.py

@@ -363,7 +363,7 @@ def main():
         xmin, ymin = stacked.min(axis=0)
         xmax, ymax = stacked.max(axis=0)
         max_length = np.max([np.abs(xmin), np.abs(ymin), np.abs(xmax), np.abs(ymax)])
-        max_length = np.ceil(max_length).astype(np.int)
+        max_length = np.ceil(max_length).astype(int)
         ins_image = Image.new("L", (2 * max_length * 100, 2 * max_length * 100), 0)
         d1 = ImageDraw.Draw(ins_image)
         sem_image = Image.new("L", (2 * max_length * 100, 2 * max_length * 100), 0)

igibson/utils/map_utils.py

@@ -41,7 +41,7 @@ def gen_trav_map(
     xmax, ymax, _ = vertices.max(axis=0)
 
     max_length = np.max([np.abs(xmin), np.abs(ymin), np.abs(xmax), np.abs(ymax)])
-    max_length = np.ceil(max_length).astype(np.int)
+    max_length = np.ceil(max_length).astype(int)
 
     wall_maps = gen_map(vertices, faces, output_folder, img_filename_format=obstacle_map_filename_format)
     wall_pts = np.array(np.where(wall_maps[0] == 0)).T
@@ -170,7 +170,7 @@ def gen_map(vertices, faces, output_folder, img_filename_format="floor_{}.png"):
     xmax, ymax, _ = vertices.max(axis=0)
 
     max_length = np.max([np.abs(xmin), np.abs(ymin), np.abs(xmax), np.abs(ymax)])
-    max_length = np.ceil(max_length).astype(np.int)
+    max_length = np.ceil(max_length).astype(int)
 
     floors = [0.0]
     print(floors)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name="igibson"
-version="2.2.1"
+version="2.2.2"
 readme = "README.md"
 requires-python = ">=2.8"
 license = {text = "MIT"}