work (#216)

mani_skill2/envs/sapien_env.py

@@ -961,9 +961,10 @@ def render_human(self):
         if self._viewer is None:
             self._viewer = Viewer()
             self._setup_viewer()
-            self._viewer.set_camera_pose(
-                self._human_render_cameras["render_camera"].camera.global_pose
-            )
+            if "render_camera" in self._human_render_cameras:
+                self._viewer.set_camera_pose(
+                    self._human_render_cameras["render_camera"].camera.global_pose
+                )
         for obj in self._hidden_objects:
             obj.show_visual()
         if physx.is_gpu_enabled() and self._scene._gpu_sim_initialized:

mani_skill2/envs/tasks/__init__.py

@@ -1,3 +1,5 @@
+from .dexterity import RotateValveEnv
+from .empty_env import EmptyEnv
 from .fmb.fmb import FMBAssembly1Env
 from .open_cabinet_drawer import OpenCabinetDoorEnv, OpenCabinetDrawerEnv
 from .pick_cube import PickCubeEnv
@@ -8,4 +10,3 @@
 from .stack_cube import StackCubeEnv
 from .two_robot_pick_cube import TwoRobotPickCube
 from .two_robot_stack_cube import TwoRobotStackCube
-from .dexterity import RotateValveEnv

mani_skill2/envs/tasks/empty_env.py

@@ -0,0 +1,112 @@
+from collections import OrderedDict
+from typing import Any, Dict
+
+import numpy as np
+import sapien
+import torch
+
+from mani_skill2.agents.robots.fetch.fetch import Fetch
+from mani_skill2.agents.robots.panda.panda import Panda
+from mani_skill2.envs.sapien_env import BaseEnv
+from mani_skill2.sensors.camera import CameraConfig
+from mani_skill2.utils.building.ground import build_ground
+from mani_skill2.utils.registration import register_env
+from mani_skill2.utils.sapien_utils import look_at
+from mani_skill2.utils.structs.types import GPUMemoryConfig, SimConfig
+
+
+@register_env("Empty-v1", max_episode_steps=200000)
+class EmptyEnv(BaseEnv):
+    """
+    Task Description
+    ----------------
+    This is just a dummy environment for showcasing robots in a empty scene
+
+    Randomizations
+    --------------
+    None
+
+    Success Conditions
+    ------------------
+    None
+
+    Visualization: link to a video/gif of the task being solved
+    """
+
+    SUPPORTED_ROBOTS = ["panda", "fetch", "xmate3_robotiq", "anymal"]
+    # agent: Union[Panda, Fetch]
+
+    def __init__(self, *args, robot_uids="panda", **kwargs):
+        super().__init__(*args, robot_uids=robot_uids, **kwargs)
+
+    def _register_sensors(self):
+        pose = look_at(eye=[0.3, 0, 0.6], target=[-0.1, 0, 0.1])
+        return [
+            CameraConfig("base_camera", pose.p, pose.q, 128, 128, np.pi / 2, 0.01, 10)
+        ]
+
+    def _register_human_render_cameras(self):
+        pose = look_at([0.75, -0.75, 0.5], [0.0, 0.0, 0.2])
+        return CameraConfig("render_camera", pose.p, pose.q, 2048, 2048, 1, 0.01, 10)
+
+    def _load_actors(self):
+        build_ground(self._scene)
+
+    def _initialize_actors(self, env_idx: torch.Tensor):
+        if self.robot_uids == "panda":
+            qpos = np.array(
+                [
+                    0.0,
+                    0.0,
+                    0,
+                    -np.pi * 6 / 8,
+                    0,
+                    np.pi * 3 / 4,
+                    np.pi / 4,
+                    0.04,
+                    0.04,
+                ]
+            )
+            self.agent.robot.set_qpos(qpos)
+        elif self.robot_uids == "xmate3_robotiq":
+            qpos = np.array(
+                [0, np.pi / 6, 0, np.pi / 3, 0, np.pi / 2, -np.pi / 2, 0, 0]
+            )
+            self.agent.robot.set_qpos(qpos)
+            self.agent.robot.set_pose(sapien.Pose([-0.562, 0, 0]))
+        elif self.robot_uids == "fetch":
+            qpos = np.array(
+                [
+                    0,
+                    0,
+                    0,
+                    0.04,
+                    0,
+                    0,
+                    0,
+                    -np.pi / 4,
+                    0,
+                    np.pi / 4,
+                    0,
+                    np.pi / 3,
+                    0,
+                    0.015,
+                    0.015,
+                ]
+            )
+            self.agent.robot.set_qpos(qpos)
+
+    def evaluate(self):
+        return {}
+
+    def _get_obs_extra(self, info: Dict):
+        return OrderedDict()
+
+    def compute_dense_reward(self, obs: Any, action: torch.Tensor, info: Dict):
+        return torch.zeros(self.num_envs, device=self.device)
+
+    def compute_normalized_dense_reward(
+        self, obs: Any, action: torch.Tensor, info: Dict
+    ):
+        max_reward = 1.0
+        return self.compute_dense_reward(obs=obs, action=action, info=info) / max_reward

manualtest/robot_showcase.py

@@ -0,0 +1,28 @@
+import sys
+
+import gymnasium as gym
+
+import mani_skill2.envs
+from mani_skill2.envs.sapien_env import BaseEnv
+
+if __name__ == "__main__":
+    robot = sys.argv[1]
+    # robot in ["panda", "fetch", "xmate3_robotiq"]:
+    env = gym.make(
+        "Empty-v1",
+        enable_shadow=True,
+        robot_uids=robot,
+        render_mode="human",
+        control_mode=None,
+        shader_dir="rt-fast",
+    )
+    env: BaseEnv = env.unwrapped
+    viewer = env.render()
+    viewer.paused = True
+
+    while True:
+        viewer = env.render()
+        # if viewer.window.key_press("n"):
+        #     env.close()
+        #     del env
+        #     break