fixed typo

README.md

@@ -204,7 +204,7 @@ data/outputs/2023.03.01/22.13.58_train_diffusion_unet_hybrid_pusht_image
 ```
 
 ## 🦾 Demo, Training and Eval on a Real Robot
-Make sure your UR5 robot is running and accepting command from its network interface (emergency stop button within reach at all time), your RealSense cameras plugged in to your workstation (tested with `realsense-viewer`) and your SpaceMouse connected with the `spacenavd` deamon running (verify with `systemctl status spacenavd`).
+Make sure your UR5 robot is running and accepting command from its network interface (emergency stop button within reach at all time), your RealSense cameras plugged in to your workstation (tested with `realsense-viewer`) and your SpaceMouse connected with the `spacenavd` daemon running (verify with `systemctl status spacenavd`).
 
 Start the demonstration collection script. Press "C" to start recording. Use SpaceMouse to move the robot. Press "S" to stop recording. 
 ```console

diffusion_policy/common/pose_trajectory_interpolator.py

@@ -28,7 +28,7 @@ class PoseTrajectoryInterpolator:
             poses = np.array(poses)
 
         if len(times) == 1:
-            # special treatement for single step interpolation
+            # special treatment for single step interpolation
             self.single_step = True
             self._times = times
             self._poses = poses
@@ -69,7 +69,7 @@ class PoseTrajectoryInterpolator:
         should_keep = (start_t < times) & (times < end_t)
         keep_times = times[should_keep]
         all_times = np.concatenate([[start_t], keep_times, [end_t]])
-        # remove duplicates, Slerp requries strictly increasing x
+        # remove duplicates, Slerp requires strictly increasing x
         all_times = np.unique(all_times)
         # interpolate
         all_poses = self(all_times)

diffusion_policy/common/pymunk_override.py

@@ -96,7 +96,7 @@ class DrawOptions(pymunk.SpaceDebugDrawOptions):
 
         See pygame_util.demo.py for a full example
 
-        Since pygame uses a coordiante system where y points down (in contrast
+        Since pygame uses a coordinate system where y points down (in contrast
         to many other cases), you either have to make the physics simulation
         with Pymunk also behave in that way, or flip everything when you draw.
 
@@ -226,7 +226,7 @@ def to_pygame(p: Tuple[float, float], surface: pygame.Surface) -> Tuple[int, int
     """Convenience method to convert pymunk coordinates to pygame surface
     local coordinates.
 
-    Note that in case positive_y_is_up is False, this function wont actually do
+    Note that in case positive_y_is_up is False, this function won't actually do
     anything except converting the point to integers.
     """
     if positive_y_is_up:

diffusion_policy/common/replay_buffer.py

@@ -84,7 +84,7 @@ def get_optimal_chunks(shape, dtype,
 class ReplayBuffer:
     """
     Zarr-based temporal datastructure.
-    Assumes first dimention to be time. Only chunk in time dimension.
+    Assumes first dimension to be time. Only chunk in time dimension.
     """
     def __init__(self, 
             root: Union[zarr.Group, 
@@ -216,7 +216,7 @@ class ReplayBuffer:
         Recommended
         """
         if backend == 'numpy':
-            print('backend argument is depreacted!')
+            print('backend argument is deprecated!')
             store = None
         group = zarr.open(os.path.expanduser(zarr_path), 'r')
         return cls.copy_from_store(src_store=group.store, store=store, 

diffusion_policy/common/timestamp_accumulator.py

@@ -13,7 +13,7 @@ def get_accumulate_timestamp_idxs(
     ) -> Tuple[List[int], List[int], int]:
     """
     For each dt window, choose the first timestamp in the window.
-    Assumes timestamps sorted. One timestamp might be chosen mulitple times due to dropped frames.
+    Assumes timestamps sorted. One timestamp might be chosen multiple times due to dropped frames.
     next_global_idx should start at 0 normally, and then use the returned next_global_idx. 
     However, when overwiting previous values are desired, set last_global_idx to None.
 
@@ -60,7 +60,7 @@ def align_timestamps(
         allow_negative=True
     )
     if len(global_idxs) > len(target_global_idxs):
-        # if more steps avaliable, truncate
+        # if more steps available, truncate
         global_idxs = global_idxs[:len(target_global_idxs)]
         local_idxs = local_idxs[:len(target_global_idxs)]
     
@@ -216,7 +216,7 @@ class TimestampActionAccumulator:
                 self.action_buffer = np.resize(self.action_buffer, new_shape)
                 self.timestamp_buffer = np.resize(self.timestamp_buffer, (new_size,))
             
-            # potentally rewrite old data (as expected)
+            # potentially rewrite old data (as expected)
             self.action_buffer[global_idxs] = actions[local_idxs]
             self.timestamp_buffer[global_idxs] = timestamps[local_idxs]
             self.size = max(self.size, this_max_size)

diffusion_policy/config/train_diffusion_unet_video_workspace.yaml

@@ -39,7 +39,7 @@ policy:
 
       norm_groups: 8
       input_channel: 3
-      model_depth: 50 # ResNet 50 (18,34 not yet avaliable)
+      model_depth: 50 # ResNet 50 (18,34 not yet available)
     
     pool:
       _target_: diffusion_policy.model.ibc.global_avgpool.GlobalAvgpool

diffusion_policy/dataset/real_pusht_image_dataset.py

@@ -84,7 +84,7 @@ class RealPushTImageDataset(BaseImageDataset):
         if delta_action:
             # replace action as relative to previous frame
             actions = replay_buffer['action'][:]
-            # suport positions only at this time
+            # support positions only at this time
             assert actions.shape[1] <= 3
             actions_diff = np.zeros_like(actions)
             episode_ends = replay_buffer.episode_ends[:]
@@ -93,8 +93,8 @@ class RealPushTImageDataset(BaseImageDataset):
                 if i > 0:
                     start = episode_ends[i-1]
                 end = episode_ends[i]
-                # delta action is the difference between previous desired postion and the current
-                # it should be scheduled at the previous timestep for the curren timestep
+                # delta action is the difference between previous desired position and the current
+                # it should be scheduled at the previous timestep for the current timestep
                 # to ensure consistency with positional mode
                 actions_diff[start+1:end] = np.diff(actions[start:end], axis=0)
             replay_buffer['action'][:] = actions_diff

diffusion_policy/env/block_pushing/block_pushing.py

@@ -745,7 +745,7 @@ class BlockPush(gym.Env):
 
         WARNING: py_environment wrapper assumes environments aren't reset in their
         constructor and will often reset the environment unintentionally. It is
-        always recommeneded that you call env.reset on the tfagents wrapper before
+        always recommended that you call env.reset on the tfagents wrapper before
         playback (replaying pybullet_state).
 
         Args:

diffusion_policy/env/block_pushing/block_pushing_multimodal.py

@@ -615,7 +615,7 @@ class BlockPushMultimodal(block_pushing.BlockPush):
 
         WARNING: py_environment wrapper assumes environments aren't reset in their
         constructor and will often reset the environment unintentionally. It is
-        always recommeneded that you call env.reset on the tfagents wrapper before
+        always recommended that you call env.reset on the tfagents wrapper before
         playback (replaying pybullet_state).
 
         Args:

diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/robot/franka_robot.py

@@ -24,7 +24,7 @@ import click
 from adept_envs import base_robot
 from adept_envs.utils.config import (get_config_root_node, read_config_from_node)
 
-# obervations structure
+# observations structure
 from collections import namedtuple
 observation = namedtuple('observation', ['time', 'qpos_robot', 'qvel_robot', 'qpos_object', 'qvel_object'])
 

diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/robot_env.py

@@ -40,7 +40,7 @@ class RobotEnv(mujoco_env.MujocoEnv):
 
     # Mapping of device path to the calibration file to use. If the device path
     # is not found, the 'default' key is used.
-    # This can be overriden by subclasses.
+    # This can be overridden by subclasses.
     CALIBRATION_PATHS = {}
 
     def __init__(self,

diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/config.py

@@ -87,7 +87,7 @@ if __name__ == '__main__':
                                                   float))
     print("data:type \t", read_config_from_node(root, "data", "type", str))
 
-    # read straight from xml (dum the XML data as duh.xml for this test)
+    # read straight from xml (dumb the XML data as duh.xml for this test)
     root, root_name = get_config_root_node(config_file_name="duh.xml")
     print("Read from xml --------------------------------")
     print("limit:low \t", read_config_from_xml("duh.xml", "limits", "low",

diffusion_policy/env/kitchen/relay_policy_learning/third_party/franka/README.md

@@ -4,6 +4,6 @@ Franka panda mujoco models
 
 # Environment
 
-franka_panda.xml           |  comming soon
+franka_panda.xml           |  coming soon
 :-------------------------:|:-------------------------:
-![Alt text](franka_panda.png?raw=false "sawyer") |  comming soon
+![Alt text](franka_panda.png?raw=false "sawyer") |  coming soon

diffusion_policy/env/pusht/pusht_env.py

@@ -44,7 +44,7 @@ class PushTEnv(gym.Env):
         # Local controller params.
         self.k_p, self.k_v = 100, 20    # PD control.z
         self.control_hz = self.metadata['video.frames_per_second']
-        # legcay set_state for data compatiblity
+        # legcay set_state for data compatibility
         self.legacy = legacy
 
         # agent_pos, block_pos, block_angle
@@ -92,7 +92,7 @@ class PushTEnv(gym.Env):
         if self.damping is not None:
             self.space.damping = self.damping
         
-        # use legacy RandomState for compatiblity
+        # use legacy RandomState for compatibility
         state = self.reset_to_state
         if state is None:
             rs = np.random.RandomState(seed=seed)
@@ -163,7 +163,7 @@ class PushTEnv(gym.Env):
         inertia = pymunk.moment_for_box(mass, (50, 100))
         body = pymunk.Body(mass, inertia)
         # preserving the legacy assignment order for compatibility
-        # the order here dosn't matter somehow, maybe because CoM is aligned with body origin
+        # the order here doesn't matter somehow, maybe because CoM is aligned with body origin
         body.position = pose[:2].tolist()
         body.angle = pose[2]
         return body
@@ -210,7 +210,7 @@ class PushTEnv(gym.Env):
             pygame.event.pump()
             pygame.display.update()
 
-            # the clock is aleady ticked during in step for "human"
+            # the clock is already ticked during in step for "human"
 
 
         img = np.transpose(
@@ -255,7 +255,7 @@ class PushTEnv(gym.Env):
         # if not the same as CoM
         # therefore should be modified first.
         if self.legacy:
-            # for compatiblity with legacy data
+            # for compatibility with legacy data
             self.block.position = pos_block
             self.block.angle = rot_block
         else:
@@ -307,7 +307,7 @@ class PushTEnv(gym.Env):
         self.goal_color = pygame.Color('LightGreen')
         self.goal_pose = np.array([256,256,np.pi/4])  # x, y, theta (in radians)
 
-        # Add collision handeling
+        # Add collision handling
         self.collision_handeler = self.space.add_collision_handler(0, 0)
         self.collision_handeler.post_solve = self._handle_collision
         self.n_contact_points = 0

diffusion_policy/env/pusht/pusht_keypoints_env.py

@@ -27,7 +27,7 @@ class PushTKeypointsEnv(PushTEnv):
         ws = self.window_size
 
         if local_keypoint_map is None:
-            # create default keypoint definiton
+            # create default keypoint definition
             kp_kwargs = self.genenerate_keypoint_manager_params()
             local_keypoint_map = kp_kwargs['local_keypoint_map']
             color_map = kp_kwargs['color_map']
@@ -107,7 +107,7 @@ class PushTKeypointsEnv(PushTEnv):
         obs = kps.flatten()
         obs_mask = kps_mask.flatten()
         if not self.agent_keypoints:
-            # passing agent position when keypoints are not avaiable
+            # passing agent position when keypoints are not available
             agent_pos = np.array(self.agent.position)
             obs = np.concatenate([
                 obs, agent_pos

diffusion_policy/env/pusht/pymunk_override.py

@@ -96,7 +96,7 @@ class DrawOptions(pymunk.SpaceDebugDrawOptions):
 
         See pygame_util.demo.py for a full example
 
-        Since pygame uses a coordiante system where y points down (in contrast
+        Since pygame uses a coordinate system where y points down (in contrast
         to many other cases), you either have to make the physics simulation
         with Pymunk also behave in that way, or flip everything when you draw.
 
@@ -226,7 +226,7 @@ def to_pygame(p: Tuple[float, float], surface: pygame.Surface) -> Tuple[int, int
     """Convenience method to convert pymunk coordinates to pygame surface
     local coordinates.
 
-    Note that in case positive_y_is_up is False, this function wont actually do
+    Note that in case positive_y_is_up is False, this function won't actually do
     anything except converting the point to integers.
     """
     if positive_y_is_up:

diffusion_policy/env_runner/pusht_keypoints_runner.py

@@ -45,7 +45,7 @@ class PushTKeypointsRunner(BaseLowdimRunner):
             n_envs = n_train + n_test
 
         # handle latency step
-        # to mimic latency, we request n_latency_steps addtional steps 
+        # to mimic latency, we request n_latency_steps additional steps 
         # of past observations, and the discard the last n_latency_steps
         env_n_obs_steps = n_obs_steps + n_latency_steps
         env_n_action_steps = n_action_steps

diffusion_policy/env_runner/robomimic_lowdim_runner.py

@@ -91,7 +91,7 @@ class RobomimicLowdimRunner(BaseLowdimRunner):
             n_envs = n_train + n_test
 
         # handle latency step
-        # to mimic latency, we request n_latency_steps addtional steps 
+        # to mimic latency, we request n_latency_steps additional steps 
         # of past observations, and the discard the last n_latency_steps
         env_n_obs_steps = n_obs_steps + n_latency_steps
         env_n_action_steps = n_action_steps

diffusion_policy/gym_util/multistep_wrapper.py

@@ -39,10 +39,10 @@ def dict_take_last_n(x, n):
 
 def aggregate(data, method='max'):
     if method == 'max':
-        # equivelent to any
+        # equivalent to any
         return np.max(data)
     elif method == 'min':
-        # equivelent to all
+        # equivalent to all
         return np.min(data)
     elif method == 'mean':
         return np.mean(data)

diffusion_policy/model/bet/action_ae/discretizers/k_means.py

@@ -41,7 +41,7 @@ class KMeansDiscretizer(DictOfTensorMixin):
     @classmethod
     def _kmeans(cls, x: torch.Tensor, ncluster: int = 512, niter: int = 50):
         """
-        Simple k-means clustering algorithm adapted from Karpathy's minGPT libary
+        Simple k-means clustering algorithm adapted from Karpathy's minGPT library
         https://github.com/karpathy/minGPT/blob/master/play_image.ipynb
         """
         N, D = x.size()

diffusion_policy/model/common/rotation_transformer.py

@@ -21,7 +21,7 @@ class RotationTransformer:
         """
         Valid representations
 
-        Alwasy use matrix as intermediate representation.
+        Always use matrix as intermediate representation.
         """
         assert from_rep != to_rep
         assert from_rep in self.valid_reps

diffusion_policy/real_world/rtde_interpolation_controller.py

@@ -21,8 +21,8 @@ class Command(enum.Enum):
 
 class RTDEInterpolationController(mp.Process):
     """
-    To ensure sending commnd to the robot with predictable latency
-    this controller need its seperate process (due to python GIL)
+    To ensure sending command to the robot with predictable latency
+    this controller need its separate process (due to python GIL)
     """
 
 
@@ -54,7 +54,7 @@ class RTDEInterpolationController(mp.Process):
         tcp_offset_pose: 6d pose
         payload_mass: float
         payload_cog: 3d position, center of gravity
-        soft_real_time: enables round-robbin scheduling and real-time priority
+        soft_real_time: enables round-robin scheduling and real-time priority
             requires running scripts/rtprio_setup.sh before hand.
 
         """

diffusion_policy/real_world/spacemouse.py

@@ -8,7 +8,7 @@ import time
 class Spacemouse(Thread):
     def __init__(self, max_value=500, deadzone=(0,0,0,0,0,0), dtype=np.float32):
         """
-        Continously listen to 3D connecion space naviagtor events
+        Continuously listen to 3D connection space naviagtor events
         and update the latest state.
 
         max_value: {300, 500} 300 for wired version and 500 for wireless

diffusion_policy/real_world/spacemouse_shared_memory.py

@@ -15,7 +15,7 @@ class Spacemouse(mp.Process):
             n_buttons=2,
             ):
         """
-        Continously listen to 3D connecion space naviagtor events
+        Continuously listen to 3D connection space naviagtor events
         and update the latest state.
 
         max_value: {300, 500} 300 for wired version and 500 for wireless
@@ -36,7 +36,7 @@ class Spacemouse(mp.Process):
             deadzone = np.array(deadzone, dtype=dtype)
         assert (deadzone >= 0).all()
 
-        # copied varaibles
+        # copied variables
         self.frequency = frequency
         self.max_value = max_value
         self.dtype = dtype

diffusion_policy/real_world/video_recorder.py

@@ -128,7 +128,7 @@ class VideoRecorder:
                 dt=1/self.fps,
                 next_global_idx=self.next_global_idx
             )
-            # number of apperance means repeats
+            # number of appearance means repeats
             n_repeats = len(local_idxs)
         
         if self.shape is None:

diffusion_policy/scripts/episode_lengths.py

@@ -16,8 +16,8 @@ from diffusion_policy.common.replay_buffer import ReplayBuffer
 @click.option('--dt', default=0.1, type=float)
 def main(input, dt):
     buffer = ReplayBuffer.create_from_path(input)
-    lenghts = buffer.episode_lengths
-    durations = lenghts * dt
+    lengths = buffer.episode_lengths
+    durations = lengths * dt
     result = {
         'duration/mean': np.mean(durations)
     }

diffusion_policy/scripts/real_pusht_successrate.py

@@ -16,7 +16,7 @@ import json
 @click.command()
 @click.option(
     '--reference', '-r', required=True,
-    help='Reference metrics_raw.json from demostration dataset.'
+    help='Reference metrics_raw.json from demonstration dataset.'
 )
 @click.option(
     '--input', '-i', required=True,

diffusion_policy/shared_memory/shared_memory_ring_buffer.py

@@ -25,7 +25,7 @@ class SharedMemoryRingBuffer:
         ):
         """
         shm_manager: Manages the life cycle of share memories 
-            across processes. Remeber to run .start() before passing.
+            across processes. Remember to run .start() before passing.
         array_specs: Name, shape and type of arrays for a single time step.
         get_max_k: The maxmum number of items can be queried at once.
         get_time_budget: The maxmum amount of time spent copying data from 

ray_exec.py

@@ -40,7 +40,7 @@ def worker_fn(command_args, data_src=None, unbuffer_python=False, use_shell=Fals
     if use_shell:
         command_args = ' '.join(command_args)
 
-    # stdout passtrough to ray worker, which is then passed to ray driver
+    # stdout passthrough to ray worker, which is then passed to ray driver
     process = subprocess.Popen(
         args=command_args, 
         env=process_env,