Merge pull request #137 from ARISE-Initiative/bug-fixes

Greatly Improved Interfaces + Bug Fixes

README.md

@@ -4,6 +4,10 @@
 
 [**[Homepage]**](https://robosuite.ai/)   [**[White Paper]**](https://arxiv.org/abs/2009.12293)   [**[Documentations]**](https://robosuite.ai/docs/overview.html)   [**[ARISE Initiative]**](https://github.com/ARISE-Initiative)
 
+-------
+## Latest Updates
+[12/17/2020] **v1.1.0**: Refactored infrastructure and standardized model classes for much easier environment prototyping :wrench:
+
 -------
 
 **robosuite** is a simulation framework powered by the [MuJoCo](http://mujoco.org/) physics engine for robot learning. It also offers a suite of benchmark environments for reproducible research. The current release (v1.0) features manipulation tasks with feature supports of procedural generation, advanced controllers, teleoperation, etc. This project is part of the broader [Advancing Robot Intelligence through Simulated Environments (ARISE) Initiative](https://github.com/ARISE-Initiative), with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics.

docs/references.md

@@ -13,6 +13,8 @@ A list of references of projects and papers that use **robosuite**. If you would
 - [Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors](https://arxiv.org/abs/2006.13205). Karl Pertsch, Oleh Rybkin, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
 - [Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty](https://arxiv.org/abs/2003.02740). Yongle Luo, Kun Dong, Lili Zhao, Zhiyong Sun, Chao Zhou, Bo Song
 - [Hierarchical 6-DoF Grasping with Approaching Direction Selection](http://rllab.snu.ac.kr/publications/papers/2020_icra_gads.pdf). Yunho Choi, Hogun Kee, Kyungjae Lee, JaeGoo Choy, Junhong Min, Sohee Lee, and Songhwai Oh
+- [Conservative Safety Critics for Exploration](https://arxiv.org/abs/2010.14497). Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg
+- [Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives](https://arxiv.org/abs/2008.13223). Oren Spector, Miriam Zacksenhouse
 
 ## Imitation Learning and Batch (Offline) Reinforcement Learning
 
@@ -21,6 +23,8 @@ A list of references of projects and papers that use **robosuite**. If you would
 - [To Follow or not to Follow: Selective Imitation Learning from Observations](https://arxiv.org/abs/1912.07670). Youngwoon Lee, Edward S. Hu, Zhengyu Yang, Joseph J. Lim
 - [Learning Robot Skills with Temporal Variational Inference](https://arxiv.org/abs/2006.16232). Tanmay Shankar, Abhinav Gupta
 - [Residual Learning from Demonstration](https://arxiv.org/abs/2008.07682). Todor Davchev, Kevin Sebastian Luck, Michael Burke, Franziska Meier, Stefan Schaal, Subramanian Ramamoorthy
+- [Variational Imitation Learning with Diverse-quality Demonstrations](https://proceedings.icml.cc/static/paper_files/icml/2020/577-Paper.pdf). Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
+- [Transformers for One-Shot Visual Imitation](https://corlconf.github.io/paper_463/). Sudeep Dasari, Abhinav Gupta
 
 ## Benchmarks
 

robosuite/__init__.py

@@ -1,21 +1,22 @@
 from robosuite.environments.base import make
 
-from robosuite.environments.lift import Lift
-from robosuite.environments.stack import Stack
-from robosuite.environments.nut_assembly import NutAssembly
-from robosuite.environments.pick_place import PickPlace
-from robosuite.environments.door import Door
-from robosuite.environments.wipe import Wipe
-from robosuite.environments.two_arm_lift import TwoArmLift
-from robosuite.environments.two_arm_peg_in_hole import TwoArmPegInHole
-from robosuite.environments.two_arm_handover import TwoArmHandover
+# Manipulation environments
+from robosuite.environments.manipulation.lift import Lift
+from robosuite.environments.manipulation.stack import Stack
+from robosuite.environments.manipulation.nut_assembly import NutAssembly
+from robosuite.environments.manipulation.pick_place import PickPlace
+from robosuite.environments.manipulation.door import Door
+from robosuite.environments.manipulation.wipe import Wipe
+from robosuite.environments.manipulation.two_arm_lift import TwoArmLift
+from robosuite.environments.manipulation.two_arm_peg_in_hole import TwoArmPegInHole
+from robosuite.environments.manipulation.two_arm_handover import TwoArmHandover
 
 from robosuite.environments import ALL_ENVIRONMENTS
 from robosuite.controllers import ALL_CONTROLLERS, load_controller_config
 from robosuite.robots import ALL_ROBOTS
 from robosuite.models.grippers import ALL_GRIPPERS
 
-__version__ = "1.0.0"
+__version__ = "1.1.0"
 __logo__ = """
       ;     /        ,--.
      ["]   ["]  ,<  |__**|

robosuite/controllers/joint_pos.py

@@ -120,7 +120,7 @@ def __init__(self,
         self.output_min = self.nums2array(output_min, self.control_dim)
 
         # limits
-        self.position_limits = qpos_limits
+        self.position_limits = np.array(qpos_limits) if qpos_limits is not None else qpos_limits
 
         # kp kd
         self.kp = self.nums2array(kp, self.control_dim)

robosuite/controllers/joint_tor.py

@@ -87,7 +87,7 @@ def __init__(self,
         self.output_min = self.nums2array(output_min, self.control_dim)
 
         # limits (if not specified, set them to actuator limits by default)
-        self.torque_limits = torque_limits if torque_limits is not None else self.actuator_limits
+        self.torque_limits = np.array(torque_limits) if torque_limits is not None else self.actuator_limits
 
         # control frequency
         self.control_freq = policy_freq

robosuite/controllers/joint_vel.py

@@ -104,7 +104,7 @@ def __init__(self,
         self.last_joint_vel = np.zeros(self.joint_dim)
 
         # limits
-        self.velocity_limits = velocity_limits
+        self.velocity_limits = np.array(velocity_limits) if velocity_limits is not None else None
 
         # control frequency
         self.control_freq = policy_freq

robosuite/controllers/osc.py

@@ -176,8 +176,8 @@ def __init__(self,
             self.control_dim += 6
 
         # limits
-        self.position_limits = position_limits
-        self.orientation_limits = orientation_limits
+        self.position_limits = np.array(position_limits) if position_limits is not None else position_limits
+        self.orientation_limits = np.array(orientation_limits) if orientation_limits is not None else orientation_limits
 
         # control frequency
         self.control_freq = policy_freq
@@ -345,6 +345,13 @@ def run_controller(self):
 
         return self.torques
 
+    def update_initial_joints(self, initial_joints):
+        # First, update from the superclass method
+        super().update_initial_joints(initial_joints)
+
+        # We also need to reset the goal in case the old goals were set to the initial confguration
+        self.reset_goal()
+
     def reset_goal(self):
         """
         Resets the goal to the current state of the robot

robosuite/demos/demo_device_control.py

@@ -100,6 +100,7 @@
 import robosuite as suite
 from robosuite import load_controller_config
 from robosuite.utils.input_utils import input2action
+from robosuite.wrappers import VisualizationWrapper
 
 
 
@@ -152,12 +153,14 @@
         render_camera="agentview",
         ignore_done=True,
         use_camera_obs=False,
-        gripper_visualizations=True,
         reward_shaping=True,
         control_freq=20,
         hard_reset=False,
     )
 
+    # Wrap this environment in a visualization wrapper
+    env = VisualizationWrapper(env, indicator_configs=None)
+
     # Setup printing options for numbers
     np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
 

robosuite/demos/demo_domain_randomization.py

@@ -2,11 +2,14 @@
 Script to showcase domain randomization functionality.
 """
 
-import numpy as np
+import robosuite.utils.macros as macros
 from robosuite.controllers import load_controller_config
 from robosuite.utils.input_utils import *
 from robosuite.wrappers import DomainRandomizationWrapper
 
+# We'll use instance randomization so that entire geom groups are randomized together
+macros.USING_INSTANCE_RANDOMIZATION = True
+
 if __name__ == "__main__":
 
     # Create dict to hold options that will be passed to env creation call

robosuite/demos/demo_gripper_interaction.py

@@ -29,16 +29,21 @@
 
     # add a gripper
     gripper = RethinkGripper()
+    # Create another body with a slider joint to which we'll add this gripper
     gripper_body = ET.Element("body")
-    for body in gripper.worldbody:
-        gripper_body.append(body)
     gripper_body.set("pos", "0 0 0.3")
     gripper_body.set("quat", "0 0 1 0")  # flip z
     gripper_body.append(
         new_joint(name="gripper_z_joint", type="slide", axis="0 0 1", damping="50")
     )
-    world.merge(gripper, merge_body=False)
+    # Add all gripper bodies to this higher level body
+    for body in gripper.worldbody:
+        gripper_body.append(body)
+    # Merge the all of the gripper tags except its bodies
+    world.merge(gripper, merge_body=None)
+    # Manually add the higher level body we created
     world.worldbody.append(gripper_body)
+    # Create a new actuator to control our slider joint
     world.actuator.append(
         new_actuator(
             joint="gripper_z_joint", act_type="position", name="gripper_z", kp="500"
@@ -51,22 +56,19 @@
         size=[0.02, 0.02, 0.02],
         rgba=[1, 0, 0, 1],
         friction=[1, 0.005, 0.0001]
-    ).get_collision()
-    mujoco_object.append(new_joint(name="object_free_joint", type="free"))
+    ).get_obj()
+    # Set the position of this object
     mujoco_object.set("pos", "0 0 0.11")
-    geoms = mujoco_object.findall("./geom")
-    for geom in geoms:
-        if geom.get("contype"):
-            pass
-        geom.set("name", "object")
-        geom.set("density", "10000")  # 1000 for water
+    # Add our object to the world body
     world.worldbody.append(mujoco_object)
 
     # add reference objects for x and y axes
-    x_ref = BoxObject(name="x_ref", size=[0.01, 0.01, 0.01], rgba=[0, 1, 0, 1]).get_visual()
+    x_ref = BoxObject(name="x_ref", size=[0.01, 0.01, 0.01], rgba=[0, 1, 0, 1], obj_type="visual",
+                      joints=None).get_obj()
     x_ref.set("pos", "0.2 0 0.105")
     world.worldbody.append(x_ref)
-    y_ref = BoxObject(name="y_ref", size=[0.01, 0.01, 0.01], rgba=[0, 0, 1, 1]).get_visual()
+    y_ref = BoxObject(name="y_ref", size=[0.01, 0.01, 0.01], rgba=[0, 0, 1, 1], obj_type="visual",
+                      joints=None).get_obj()
     y_ref.set("pos", "0 0.2 0.105")
     world.worldbody.append(y_ref)
 
@@ -83,6 +85,7 @@
         sim.model.get_joint_qvel_addr(x) for x in gravity_corrected
     ]
 
+    # Set gripper parameters
     gripper_z_id = sim.model.actuator_name2id("gripper_z")
     gripper_z_low = 0.07
     gripper_z_high = -0.02
@@ -102,11 +105,10 @@
     step = 0
     T = 500
     while True:
-
         if step % 100 == 0:
             print("step: {}".format(step))
 
-        if step % 100 == 0:
+            # Get contact information
             for contact in sim.data.contact[0 : sim.data.ncon]:
 
                 geom_name1 = sim.model.geom_id2name(contact.geom1)
@@ -119,6 +121,7 @@
                 print("friction: {}".format(contact.friction))
                 print("normal: {}".format(contact.frame[0:3]))
 
+        # Iterate through gripping trajectory
         if step % T == 0:
             plan = seq[int(step / T) % len(seq)]
             gripper_z_is_low, gripper_is_closed = plan
@@ -128,6 +131,7 @@
                 )
             )
 
+        # Control gripper
         if gripper_z_is_low:
             sim.data.ctrl[gripper_z_id] = gripper_z_low
         else:
@@ -137,6 +141,7 @@
         else:
             sim.data.ctrl[gripper_jaw_ids] = gripper_open
 
+        # Step through sim
         sim.step()
         sim.data.qfrc_applied[_ref_joint_vel_indexes] = sim.data.qfrc_bias[
             _ref_joint_vel_indexes

robosuite/demos/demo_video_recording.py

@@ -10,8 +10,12 @@
 import imageio
 import numpy as np
 
+import robosuite.utils.macros as macros
 from robosuite import make
 
+# Set the image convention to opencv so that the images are automatically rendered "right side up" when using imageio
+# (which uses opencv convention)
+macros.IMAGE_CONVENTION = "opencv"
 
 if __name__ == "__main__":
 
@@ -54,7 +58,7 @@
 
         # dump a frame from every K frames
         if i % args.skip_frame == 0:
-            frame = obs[args.camera + "_image"][::-1]
+            frame = obs[args.camera + "_image"]
             writer.append_data(frame)
             print("Saving frame #{}".format(i))
 

robosuite/devices/spacemouse.py

@@ -123,6 +123,10 @@ def __init__(self,
         print("Manufacturer: %s" % self.device.get_manufacturer_string())
         print("Product: %s" % self.device.get_product_string())
 
+        # 6-DOF variables
+        self.x, self.y, self.z = 0, 0, 0
+        self.roll, self.pitch, self.yaw = 0, 0, 0
+
         self._display_controls()
 
         self.single_click_and_hold = False
@@ -164,6 +168,13 @@ def _reset_internal_state(self):
         Resets internal state of controller, except for the reset signal.
         """
         self.rotation = np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., -1.]])
+        # Reset 6-DOF variables
+        self.x, self.y, self.z = 0, 0, 0
+        self.roll, self.pitch, self.yaw = 0, 0, 0
+        # Reset control
+        self._control = np.zeros(6)
+        # Reset grasp
+        self.single_click_and_hold = False
 
     def start_control(self):
         """
@@ -183,7 +194,6 @@ def get_controller_state(self):
         """
         dpos = self.control[:3] * 0.005 * self.pos_sensitivity
         roll, pitch, yaw = self.control[3:] * 0.005 * self.rot_sensitivity
-        self.grasp = self.control_gripper
 
         # convert RPY to an absolute orientation
         drot1 = rotation_matrix(angle=-pitch, direction=[1., 0, 0], point=None)[:3, :3]
@@ -196,7 +206,7 @@ def get_controller_state(self):
             dpos=dpos,
             rotation=self.rotation,
             raw_drotation=np.array([roll, pitch, yaw]),
-            grasp=self.grasp,
+            grasp=self.control_gripper,
             reset=self._reset_state
         )