feat: make torch an optional dependency for the batched simulator

torch, torchdiffeq, roma, and opt-einsum are only required for the
batched simulator (BatchedMultirotor, simulate_batch, BatchedSE3Control,
etc.). Moving them to an optional extra avoids forcing all users to
install PyTorch (~2 GB) when they only need the standard single-drone
simulator.

- pyproject.toml: remove the four packages from core dependencies and
  add a new `batched` extra; also include them in `all`
- Wrap top-level torch/roma/torchdiffeq imports with try/except in
  every file that mixes batched and non-batched classes, so those
  modules remain importable without the extra installed

Install the batched simulator with: pip install rotorpy[batched]

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: Tuxliri

pyproject.toml

@@ -29,14 +29,16 @@ dependencies = [
     'pandas',
     'tqdm',
     'gymnasium',
-    'roma',           # For batched sim
-    'torch>=1.11.0',  # For batched sim
-    'torchdiffeq',    # For batched sim
-    'opt-einsum',     # For batched sim
     'timed_count',    # Only for ardupilot sitl example
 ]
 
 [project.optional-dependencies]
+batched = [
+    'torch>=1.11.0',
+    'torchdiffeq',
+    'roma',
+    'opt-einsum',
+]
 learning = [
     'stable_baselines3',
     'tensorboard',
@@ -64,6 +66,10 @@ px4 = [
     'pymavlink',
 ]
 all = [
+    "torch>=1.11.0",
+    "torchdiffeq",
+    "roma",
+    "opt-einsum",
     "stable_baselines3",
     "tensorboard",
     "pytest",

rotorpy/controllers/quadrotor_control.py

@@ -1,6 +1,9 @@
 import numpy as np
-import torch
-import roma
+try:
+    import torch
+    import roma
+except ImportError:
+    pass
 from scipy.spatial.transform import Rotation
 
 class SE3Control(object):

rotorpy/sensors/imu.py

@@ -1,6 +1,9 @@
 import numpy as np
 from scipy.spatial.transform import Rotation
-import torch
+try:
+    import torch
+except ImportError:
+    pass
 import copy
 
 class Imu:

rotorpy/simulate.py

@@ -2,8 +2,11 @@
 from enum import Enum
 import copy
 import numpy as np
-import roma
-import torch
+try:
+    import roma
+    import torch
+except ImportError:
+    pass
 from numpy.linalg import norm
 from scipy.spatial.transform import Rotation
 from time import perf_counter

rotorpy/trajectories/circular_traj.py

@@ -1,5 +1,8 @@
 import numpy as np
-import torch
+try:
+    import torch
+except ImportError:
+    pass
 import sys
 
 class ThreeDCircularTraj(object):

rotorpy/trajectories/hover_traj.py

@@ -1,5 +1,8 @@
 import numpy as np
-import torch
+try:
+    import torch
+except ImportError:
+    pass
 
 class HoverTraj(object):
     """

rotorpy/trajectories/lissajous_traj.py

@@ -1,5 +1,8 @@
 import numpy as np
-import torch
+try:
+    import torch
+except ImportError:
+    pass
 
 """
 Lissajous curves are defined by trigonometric functions parameterized in time. 

rotorpy/trajectories/minsnap.py

@@ -5,7 +5,10 @@
 import cvxopt
 from scipy.linalg import block_diag
 from typing import List
-import torch
+try:
+    import torch
+except ImportError:
+    pass
 
 def cvxopt_solve_qp(P, q, G=None, h=None, A=None, b=None):
     """

rotorpy/trajectories/traj_template.py

@@ -2,7 +2,10 @@
 Imports
 """
 import numpy as np
-import torch
+try:
+    import torch
+except ImportError:
+    pass
 
 class TrajTemplate(object):
     """

rotorpy/vehicles/multirotor.py

@@ -8,9 +8,12 @@
 from scipy.spatial.transform import Rotation as R
 
 # imports for Batched Dynamics
-import torch
-from torchdiffeq import odeint
-import roma
+try:
+    import torch
+    from torchdiffeq import odeint
+    import roma
+except ImportError:
+    pass
 
 import time