[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

get_started/rl/fast_td3/fttd3_module.py

@@ -425,7 +425,8 @@ def projection(
         next_dist = F.softmax(self.forward(obs, actions), dim=1)
         proj_dist = torch.zeros_like(next_dist)
         offset = (
-            torch.linspace(0, (batch_size - 1) * self.num_atoms, batch_size, device=device)
+            torch
+            .linspace(0, (batch_size - 1) * self.num_atoms, batch_size, device=device)
             .unsqueeze(1)
             .expand(batch_size, self.num_atoms)
             .long()

metasim/randomization/core/isaacsim_adapter.py

@@ -263,7 +263,7 @@ def apply_mdl_material(
         # - material_name: Variant within MDL (optional, e.g., "Plywood::Oak")
         # - mtl_prim_name: Unique prim name to avoid conflicts
         mdl_basename = os.path.basename(mdl_path).removesuffix(".mdl")
-        prim_basename = prim_path.split("/")[-1]
+        prim_basename = prim_path.rsplit("/", maxsplit=1)[-1]
         mtl_prim_unique = f"{mdl_basename}_{prim_basename}_{id(prim_path)}"  # Fully unique
 
         _, mtl_prim_path = get_material_prim_path(mtl_prim_unique)

metasim/sim/newton/newton.py

@@ -1178,7 +1178,8 @@ def _render_tiled_cameras(self, env_ids: list[int]) -> dict:
                             depth_tensor = depth_tensor[use_env_ids]
 
                     intrinsics = (
-                        torch.tensor(cam_cfg.intrinsics, dtype=torch.float32, device=self.device)
+                        torch
+                        .tensor(cam_cfg.intrinsics, dtype=torch.float32, device=self.device)
                         .unsqueeze(0)
                         .expand(len(use_env_ids), -1, -1)
                     )

metasim/utils/kinematics.py

@@ -103,7 +103,8 @@ def _solve_ik_jax_with_seed(robot, target_link_index, target_wxyz, target_positi
         ]
 
         sol = (
-            jaxls.LeastSquaresProblem(factors, [joint_var])
+            jaxls
+            .LeastSquaresProblem(factors, [joint_var])
             .analyze()
             .solve(
                 initial_vals=jaxls.VarValues.make([joint_var.with_value(prev_cfg)]),
@@ -130,7 +131,8 @@ def _solve_ik_jax_without_seed(robot, target_link_index, target_wxyz, target_pos
         ]
 
         sol = (
-            jaxls.LeastSquaresProblem(factors, [joint_var])
+            jaxls
+            .LeastSquaresProblem(factors, [joint_var])
             .analyze()
             .solve(
                 verbose=False,

roboverse_pack/callback_funcs/humanoid/step_funcs.py

@@ -77,7 +77,8 @@ def push_by_setting_velocity(
             / env.step_dt
         ).to(torch.int)
     push_env_ids = (
-        torch.logical_and(env._episode_steps % env.push_interval == 0, env._episode_steps > 0)
+        torch
+        .logical_and(env._episode_steps % env.push_interval == 0, env._episode_steps > 0)
         .nonzero(as_tuple=False)
         .flatten()
     )

roboverse_pack/tasks/beyondmimic/metasim/envs/base_legged_robot.py

@@ -129,7 +129,8 @@ def _instantiate_cfg(self):
 
         # per-actuator action scale
         self.action_scale = (
-            torch.tensor(list(self.robot.action_scale.values()), device=self.device)
+            torch
+            .tensor(list(self.robot.action_scale.values()), device=self.device)
             .unsqueeze(0)
             .repeat(self.num_envs, 1)
         )

roboverse_pack/tasks/beyondmimic/metasim/mdp/events.py

@@ -66,7 +66,8 @@ def push_by_setting_velocity(
         ).to(torch.int)
     # TODO different from how interval events are triggered in Isaac Lab, consider adapting this
     push_env_ids = (
-        torch.logical_and(env._episode_steps % env.push_interval == 0, env._episode_steps > 0)
+        torch
+        .logical_and(env._episode_steps % env.push_interval == 0, env._episode_steps > 0)
         .nonzero(as_tuple=False)
         .flatten()
     )

roboverse_pack/tasks/beyondmimic/metasim/mdp/rewards.py

@@ -109,9 +109,8 @@ def undesired_contacts(
     contact_forces: ContactForces = env_states.extras["contact_forces"][env.name]
     is_contact = (
         # TODO check correspondence with `contact_sensor.data.net_forces_w_history`
-        contact_forces.contact_forces_history[
-            :, :, indexes, :
-        ]  # [n_envs, history_length, n_indexes, 3] -> [n_envs, 3, 26, 3]
+        contact_forces
+        .contact_forces_history[:, :, indexes, :]  # [n_envs, history_length, n_indexes, 3] -> [n_envs, 3, 26, 3]
         .norm(dim=-1)
         .max(dim=1)[0]
         > threshold