diff --git a/legged_gym/envs/base/legged_robot.py b/legged_gym/envs/base/legged_robot.py
index 2652f91..98ae04d 100644
--- a/legged_gym/envs/base/legged_robot.py
+++ b/legged_gym/envs/base/legged_robot.py
@@ -13,8 +13,8 @@ from typing import Tuple, Dict
 
 from legged_gym import LEGGED_GYM_ROOT_DIR
 from legged_gym.envs.base.base_task import BaseTask
-from legged_gym.utils.math import quat_apply_yaw, wrap_to_pi, torch_rand_sqrt_float
-from legged_gym.utils.isaacgym_utils import get_euler_xyz
+from legged_gym.utils.math import wrap_to_pi
+from legged_gym.utils.isaacgym_utils import get_euler_xyz as get_euler_xyz_in_tensor
 from legged_gym.utils.helpers import class_to_dict
 from .legged_robot_config import LeggedRobotCfg
 
@@ -93,7 +93,7 @@ class LeggedRobot(BaseTask):
         # prepare quantities
         self.base_pos[:] = self.root_states[:, 0:3]
         self.base_quat[:] = self.root_states[:, 3:7]
-        self.rpy[:] = get_euler_xyz(self.base_quat)
+        self.rpy[:] = get_euler_xyz_in_tensor(self.base_quat[:])
         self.base_lin_vel[:] = quat_rotate_inverse(self.base_quat, self.root_states[:, 7:10])
         self.base_ang_vel[:] = quat_rotate_inverse(self.base_quat, self.root_states[:, 10:13])
         self.projected_gravity[:] = quat_rotate_inverse(self.base_quat, self.gravity_vec)
@@ -427,7 +427,7 @@ class LeggedRobot(BaseTask):
         self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
         self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
         self.base_quat = self.root_states[:, 3:7]
-        self.rpy = get_euler_xyz(self.base_quat)
+        self.rpy = get_euler_xyz_in_tensor(self.base_quat)
         self.base_pos = self.root_states[:self.num_envs, 0:3]
         self.contact_forces = gymtorch.wrap_tensor(net_contact_forces).view(self.num_envs, -1, 3) # shape: num_envs, num_bodies, xyz axis
 
diff --git a/legged_gym/envs/base/legged_robot_config.py b/legged_gym/envs/base/legged_robot_config.py
index f029268..03f09a2 100644
--- a/legged_gym/envs/base/legged_robot_config.py
+++ b/legged_gym/envs/base/legged_robot_config.py
@@ -9,6 +9,7 @@ class LeggedRobotCfg(BaseConfig):
         env_spacing = 3.  # not used with heightfields/trimeshes 
         send_timeouts = True # send time out information to the algorithm
         episode_length_s = 20 # episode length in seconds
+        test = False
 
     class terrain:
         mesh_type = 'plane' # "heightfield" # none, plane, heightfield or trimesh
diff --git a/legged_gym/envs/h1/h1_config.py b/legged_gym/envs/h1/h1_config.py
index 64802b1..ba4c3a1 100644
--- a/legged_gym/envs/h1/h1_config.py
+++ b/legged_gym/envs/h1/h1_config.py
@@ -28,7 +28,6 @@ class H1RoughCfg( LeggedRobotCfg ):
     class env(LeggedRobotCfg.env):
         num_observations = 42
         num_actions = 10
-        test = False
       
 
     class control( LeggedRobotCfg.control ):
diff --git a/legged_gym/utils/isaacgym_utils.py b/legged_gym/utils/isaacgym_utils.py
index 3fd8c0c..a92c0df 100644
--- a/legged_gym/utils/isaacgym_utils.py
+++ b/legged_gym/utils/isaacgym_utils.py
@@ -3,474 +3,11 @@ import numpy as np
 import random
 import torch
 
-
-def set_seed(seed):
-    if seed == -1:
-        seed = np.random.randint(0, 10000)
-    print("Setting seed: {}".format(seed))
-
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    os.environ['PYTHONHASHSEED'] = str(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
-def to_torch(x, dtype=torch.float, device='cuda:0', requires_grad=False):
-    return torch.tensor(x,
-                        dtype=dtype,
-                        device=device,
-                        requires_grad=requires_grad)
-
-
-@torch.jit.script
-def quat_mul(a, b):
-    assert a.shape == b.shape
-    shape = a.shape
-    a = a.reshape(-1, 4)
-    b = b.reshape(-1, 4)
-
-    x1, y1, z1, w1 = a[:, 0], a[:, 1], a[:, 2], a[:, 3]
-    x2, y2, z2, w2 = b[:, 0], b[:, 1], b[:, 2], b[:, 3]
-    ww = (z1 + x1) * (x2 + y2)
-    yy = (w1 - y1) * (w2 + z2)
-    zz = (w1 + y1) * (w2 - z2)
-    xx = ww + yy + zz
-    qq = 0.5 * (xx + (z1 - x1) * (x2 - y2))
-    w = qq - ww + (z1 - y1) * (y2 - z2)
-    x = qq - xx + (x1 + w1) * (x2 + w2)
-    y = qq - yy + (w1 - x1) * (y2 + z2)
-    z = qq - zz + (z1 + y1) * (w2 - x2)
-
-    quat = torch.stack([x, y, z, w], dim=-1).view(shape)
-
-    return quat
-
-
-@torch.jit.script
-def normalize(x, eps: float = 1e-9):
-    return x / x.norm(p=2, dim=-1).clamp(min=eps, max=None).unsqueeze(-1)
-
-
-@torch.jit.script
-def quat_apply(a, b):
-    shape = b.shape
-    a = a.reshape(-1, 4)
-    b = b.reshape(-1, 3)
-    xyz = a[:, :3]
-    t = xyz.cross(b, dim=-1) * 2
-    return (b + a[:, 3:] * t + xyz.cross(t, dim=-1)).view(shape)
-
-
-@torch.jit.script
-def quat_rotate(q, v):
-    shape = q.shape
-    q_w = q[:, -1]
-    q_vec = q[:, :3]
-    a = v * (2.0 * q_w ** 2 - 1.0).unsqueeze(-1)
-    b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0
-    c = q_vec * \
-        torch.bmm(q_vec.view(shape[0], 1, 3), v.view(
-            shape[0], 3, 1)).squeeze(-1) * 2.0
-    return a + b + c
-
-
-@torch.jit.script
-def quat_rotate_inverse(q, v):
-    shape = q.shape
-    q_w = q[:, -1]
-    q_vec = q[:, :3]
-    a = v * (2.0 * q_w ** 2 - 1.0).unsqueeze(-1)
-    b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0
-    c = q_vec * \
-        torch.bmm(q_vec.view(shape[0], 1, 3), v.view(
-            shape[0], 3, 1)).squeeze(-1) * 2.0
-    return a - b + c
-
-
-@torch.jit.script
-def quat_conjugate(a):
-    shape = a.shape
-    a = a.reshape(-1, 4)
-    return torch.cat((-a[:, :3], a[:, -1:]), dim=-1).view(shape)
-
-
-@torch.jit.script
-def quat_unit(a):
-    return normalize(a)
-
-
-@torch.jit.script
-def quat_from_angle_axis(angle, axis):
-    theta = (angle / 2).unsqueeze(-1)
-    xyz = normalize(axis) * theta.sin()
-    w = theta.cos()
-    return quat_unit(torch.cat([xyz, w], dim=-1))
-
-
-@torch.jit.script
-def normalize_angle(x):
-    return torch.atan2(torch.sin(x), torch.cos(x))
-
-
-@torch.jit.script
-def tf_inverse(q, t):
-    q_inv = quat_conjugate(q)
-    return q_inv, -quat_apply(q_inv, t)
-
-
-@torch.jit.script
-def tf_apply(q, t, v):
-    return quat_apply(q, v) + t
-
-
-@torch.jit.script
-def tf_vector(q, v):
-    return quat_apply(q, v)
-
-
-@torch.jit.script
-def tf_combine(q1, t1, q2, t2):
-    return quat_mul(q1, q2), quat_apply(q1, t2) + t1
-
-
-@torch.jit.script
-def get_basis_vector(q, v):
-    return quat_rotate(q, v)
-
-
-def get_axis_params(value, axis_idx, x_value=0., dtype=np.float32, n_dims=3):
-    """construct arguments to `Vec` according to axis index.
-    """
-    zs = np.zeros((n_dims,))
-    assert axis_idx < n_dims, "the axis dim should be within the vector dimensions"
-    zs[axis_idx] = 1.
-    params = np.where(zs == 1., value, zs)
-    params[0] = x_value
-    return list(params.astype(dtype))
-
-
 @torch.jit.script
 def copysign(a, b):
     # type: (float, Tensor) -> Tensor
     a = torch.tensor(a, device=b.device, dtype=torch.float).repeat(b.shape[0])
     return torch.abs(a) * torch.sign(b)
-
-
-@torch.jit.script
-def get_euler_xyz(q):
-    qx, qy, qz, qw = 0, 1, 2, 3
-    # roll (x-axis rotation)
-    sinr_cosp = 2.0 * (q[:, qw] * q[:, qx] + q[:, qy] * q[:, qz])
-    cosr_cosp = q[:, qw] * q[:, qw] - q[:, qx] * \
-                q[:, qx] - q[:, qy] * q[:, qy] + q[:, qz] * q[:, qz]
-    roll = torch.atan2(sinr_cosp, cosr_cosp)
-
-    # pitch (y-axis rotation)
-    sinp = 2.0 * (q[:, qw] * q[:, qy] - q[:, qz] * q[:, qx])
-    pitch = torch.where(
-        torch.abs(sinp) >= 1, copysign(np.pi / 2.0, sinp), torch.asin(sinp))
-
-    # yaw (z-axis rotation)
-    siny_cosp = 2.0 * (q[:, qw] * q[:, qz] + q[:, qx] * q[:, qy])
-    cosy_cosp = q[:, qw] * q[:, qw] + q[:, qx] * \
-                q[:, qx] - q[:, qy] * q[:, qy] - q[:, qz] * q[:, qz]
-    yaw = torch.atan2(siny_cosp, cosy_cosp)
-
-    return roll % (2 * np.pi), pitch % (2 * np.pi), yaw % (2 * np.pi)
-
-
-@torch.jit.script
-def quat_from_euler_xyz(roll, pitch, yaw):
-    cy = torch.cos(yaw * 0.5)
-    sy = torch.sin(yaw * 0.5)
-    cr = torch.cos(roll * 0.5)
-    sr = torch.sin(roll * 0.5)
-    cp = torch.cos(pitch * 0.5)
-    sp = torch.sin(pitch * 0.5)
-
-    qw = cy * cr * cp + sy * sr * sp
-    qx = cy * sr * cp - sy * cr * sp
-    qy = cy * cr * sp + sy * sr * cp
-    qz = sy * cr * cp - cy * sr * sp
-
-    return torch.stack([qx, qy, qz, qw], dim=-1)
-
-
-@torch.jit.script
-def torch_rand_float(lower, upper, shape, device):
-    # type: (float, float, Tuple[int, int], str) -> Tensor
-    return (upper - lower) * torch.rand(*shape, device=device) + lower
-
-
-@torch.jit.script
-def torch_random_dir_2(shape, device):
-    # type: (Tuple[int, int], str) -> Tensor
-    angle = torch_rand_float(-np.pi, np.pi, shape, device).squeeze(-1)
-    return torch.stack([torch.cos(angle), torch.sin(angle)], dim=-1)
-
-
-@torch.jit.script
-def tensor_clamp(t, min_t, max_t):
-    return torch.max(torch.min(t, max_t), min_t)
-
-
-@torch.jit.script
-def scale(x, lower, upper):
-    return (0.5 * (x + 1.0) * (upper - lower) + lower)
-
-
-@torch.jit.script
-def unscale(x, lower, upper):
-    return (2.0 * x - upper - lower) / (upper - lower)
-
-
-def unscale_np(x, lower, upper):
-    return (2.0 * x - upper - lower) / (upper - lower)
-
-
-def slope_platform_stairs_terrain(terrain,
-                                  slope=1,
-                                  step_width=0.2,
-                                  step_height=0.1,
-                                  num_steps=5):
-    """
-    Generate a sloped followed by a platform and a downstair
-
-    Parameters:
-        terrain (SubTerrain): the terrain
-        slope (int): positive or negative slope
-        step_width (float): the width of the step [meters]
-        step_height (float): the height of the step [meters]
-        num_steps (int): number of the step of the stair
-    Returns:
-        terrain (SubTerrain): update terrain
-    """
-    # switch parameters to discrete units
-    step_width = round(step_width / terrain.horizontal_scale)
-    step_height = round(step_height / terrain.vertical_scale)
-
-    height = step_height
-    for i in range(num_steps):
-        terrain.height_field_raw[i * step_width:(i + 1) *
-                                                step_width, :] += height
-        height += step_height
-
-    platform_height = height
-    slope_width = int(platform_height /
-                      (np.abs(slope) *
-                       (terrain.horizontal_scale / terrain.vertical_scale)))
-    x = np.arange(0, slope_width)
-    y = np.arange(0, terrain.length)
-    xx, yy = np.meshgrid(x, y, sparse=True)
-    xx = xx.reshape(slope_width, 1)
-    platform_width = int(terrain.width - num_steps * step_width - slope_width)
-    terrain.height_field_raw[num_steps * step_width:,
-    np.arange(terrain.length)] = platform_height
-    terrain.height_field_raw[-slope_width:,
-    np.arange(terrain.length)] -= (
-            platform_height * xx / slope_width).astype(
-        terrain.height_field_raw.dtype)
-    terrain.height_field_raw = terrain.height_field_raw[::-1]
-
-    return terrain
-
-
-def stairs_platform_slope_terrain(terrain,
-                                  step_width=0.2,
-                                  step_height=0.1,
-                                  num_steps=10,
-                                  slope=0.26):
-    """
-    Generate a stairs followed with a flat plane and a downslope
-
-    Parameters:
-        terrain (terrain): the terrain
-        step_width (float): the width of the step [meters]
-        step_height (float): the height of the step [meters]
-        num_steps (int): number of the step of the stair
-        slope (float): positive or negative slope
-    Returns:
-        terrain (SubTerrain): update terrain
-    """
-    # switch parameters to discrete units
-    step_width = round(step_width / terrain.horizontal_scale)
-    step_height = round(step_height / terrain.vertical_scale)
-
-    height = step_height
-    for i in range(num_steps):
-        terrain.height_field_raw[i * step_width:(i + 1) *
-                                                step_width, :] += height
-        height += step_height
-
-    platform_height = height
-    slope_width = int(platform_height /
-                      (np.abs(slope) *
-                       (terrain.horizontal_scale / terrain.vertical_scale)))
-    x = np.arange(0, slope_width)
-    y = np.arange(0, terrain.length)
-    xx, yy = np.meshgrid(x, y, sparse=True)
-    xx = xx.reshape(slope_width, 1)
-    platform_width = int(terrain.width - num_steps * step_width - slope_width)
-    terrain.height_field_raw[num_steps * step_width:,
-    np.arange(terrain.length)] = platform_height
-    terrain.height_field_raw[-slope_width:,
-    np.arange(terrain.length)] -= (
-            platform_height * xx / slope_width).astype(
-        terrain.height_field_raw.dtype)
-
-    return terrain
-
-
-if torch.cuda.is_available():
-    import string
-    import cupy as cp
-
-
-    def compute_meshes_normals_kernel():
-        compute_meshes_normals_kernel = cp.ElementwiseKernel(
-            in_params="raw U vertices, raw T triangles",
-            out_params="raw U output",
-            preamble=string.Template("""
-                __device__ bool cross(float p1x, float p1y, float p1z,
-                                        float p2x, float p2y, float p2z,
-                                        float& normalx,float& normaly,float& normalz)
-                {
-                    float nx = p1y*p2z - p1z*p2y;
-                    float ny = p1z*p2x - p1x*p2z;
-                    float nz = p1x*p2y - p1y*p2x;
-                    float l = sqrt(nx*nx + ny*ny + nz*nz);
-                    if (l <1e-4) {
-                        l = 1;
-                    }
-                    if (nz < 0.) {
-                        nx *=-1;
-                        ny *=-1;
-                        nz *=-1;
-                    }
-                    atomicAdd(&normalx, nx/l);
-                    atomicAdd(&normaly, ny/l);
-                    atomicAdd(&normalz, nz/l);
-
-                    return true;
-                }
-                """).substitute(),
-            operation=string.Template("""
-                int idx = i*2;
-                
-                T mesh_1_0 = triangles[idx*3];
-                T mesh_1_1 = triangles[idx*3+1];
-                T mesh_1_2 = triangles[idx*3+2];
-                T mesh_2_0 = triangles[idx*3+3];
-                T mesh_2_1 = triangles[idx*3+4];
-                T mesh_2_2 = triangles[idx*3+5];
-
-                U p01x = vertices[mesh_1_1*3] - vertices[mesh_1_0*3];
-                U p01y = vertices[mesh_1_1*3+1] - vertices[mesh_1_0*3+1];
-                U p01z = vertices[mesh_1_1*3+2] - vertices[mesh_1_0*3+2];
-                U p02x = vertices[mesh_1_2*3] - vertices[mesh_1_0*3];
-                U p02y = vertices[mesh_1_2*3+1] - vertices[mesh_1_0*3+1];
-                U p02z = vertices[mesh_1_2*3+2] - vertices[mesh_1_0*3+2];
-                cross(p01x, p01y, p01z, p02x, p02y, p02z, output[3*i], output[3*i+1],output[3*i+2]);
-                
-                p01x = vertices[mesh_2_1*3] - vertices[mesh_2_0*3];
-                p01y = vertices[mesh_2_1*3+1] - vertices[mesh_2_0*3+1];
-                p01z = vertices[mesh_2_1*3+2] - vertices[mesh_2_0*3+2];
-                p02x = vertices[mesh_2_2*3] - vertices[mesh_2_0*3];
-                p02y = vertices[mesh_2_2*3+1] - vertices[mesh_2_0*3+1];
-                p02z = vertices[mesh_2_2*3+2] - vertices[mesh_2_0*3+2];
-                cross(p01x, p01y, p01z, p02x, p02y, p02z, output[3*i], output[3*i+1],output[3*i+2]);
-                
-                output[3*i] /= 2.0;
-                output[3*i+1] /= 2.0;
-                output[3*i+2] /= 2.0;
-                
-                """).substitute(),
-            name="compute_meshes_normals_kernel",
-        )
-        return compute_meshes_normals_kernel
-
-
-    compute_meshes_normals_gpu_ = compute_meshes_normals_kernel()
-
-
-    def compute_meshes_normals_gpu(num_rows, num_cols, vertices, triangles):
-        vertices_gpu = cp.array(vertices, dtype=np.float32)
-        triangles_gpu = cp.array(triangles, dtype=np.int32)
-        output = cp.zeros((num_rows - 1, num_cols - 1, 3), dtype=np.float32)
-        compute_meshes_normals_gpu_(vertices_gpu,
-                                    triangles_gpu,
-                                    output,
-                                    size=((num_rows - 1) * (num_cols - 1)))
-        output_torch = torch.as_tensor(output)
-        return output_torch
-
-
-def compute_meshes_normals(num_rows, num_cols, vertices, triangles):
-    """
-    Compute normal vectors for all trimesh of the designed terrain
-
-    Parameters:
-        num_rows (int): Number of the row of the terrain
-        num_cols (int): Number of the column of the terrain
-        vertices (np.array(float)): array of shape (num_vertices, 3). Each row represents the location of each vertex [meters]
-        triangles (np.array(int)): array of shape (num_triangles, 3). Each row represents the indices of the 3 vertices connected by this triangle.
-    Returns:
-        normals (np.array(float)): array of shape (num_rows-1, num_cols-1, 3). Normals of the terrain
-    """
-    normals = np.zeros((num_rows - 1, num_cols - 1, 3), dtype=np.float32)
-    for i in range(num_rows - 1):
-        start = 2 * i * (num_cols - 1)
-        for j in range(num_cols - 1):
-            idx_1 = start + 2 * j
-            mesh_1, mesh_2 = triangles[idx_1], triangles[idx_1 + 1]
-            normal1 = compute_triangle_normal(vertices[mesh_1[0]],
-                                              vertices[mesh_1[1]],
-                                              vertices[mesh_1[2]])
-            normal2 = compute_triangle_normal(vertices[mesh_2[0]],
-                                              vertices[mesh_2[1]],
-                                              vertices[mesh_2[2]])
-            new_normal = (
-                                 normal1 +
-                                 normal2) / 2.0  # np.mean(np.stack((normal1, normal2)), axis=0)
-            if new_normal[2] < 0.:
-                new_normal *= -1
-            normals[i][j] = new_normal
-    return torch.from_numpy(normals)
-
-
-def compute_triangle_normal(p0, p1, p2):
-    """
-    Compute the normal vector of a trimesh
-
-    Parameters:
-        p0 (np.array(float)): array of shape (3,). Position of the trimesh's first vertice
-        p1 (np.array(float)): array of shape (3,). Position of the trimesh's second vertice
-        p2 (np.array(float)): array of shape (3,). Position of the trimesh's third vertice
-    Returns:
-        normal (np.array(float)): array of shape (3,). Normal Vector of the given trimesh
-    """
-    p0p1 = p1 - p0
-    p0p2 = p2 - p0
-    n = np.cross(p0p1, p0p2)
-    l = np.linalg.norm(n)
-    if l != 0.0:
-        normal = n / l
-    else:
-        normal = n
-    return normal
-
-
-class Point:
-
-    def __init__(self, x, y, z):
-        self.x = x
-        self.y = y
-        self.z = z
-
-
-@torch.jit.script
 def get_euler_xyz(q):
     qx, qy, qz, qw = 0, 1, 2, 3
     # roll (x-axis rotation)
@@ -491,94 +28,3 @@ def get_euler_xyz(q):
     yaw = torch.atan2(siny_cosp, cosy_cosp)
 
     return torch.stack((roll, pitch, yaw), dim=-1)
-
-
-@torch.jit.script
-def coordinate_rotation(axis: int, angle):
-    s = torch.sin(angle)
-    c = torch.cos(angle)
-
-    R = torch.eye(3, dtype=torch.float, device=angle.device)
-    R = R.reshape((1, 3, 3)).repeat(angle.size(0), 1, 1)
-
-    if axis == 0:
-        R[:, 1, 1] = c
-        R[:, 2, 2] = c
-        R[:, 1, 2] = s
-        R[:, 2, 1] = -s
-    elif axis == 1:
-        R[:, 0, 0] = c
-        R[:, 0, 2] = -s
-        R[:, 2, 0] = s
-        R[:, 2, 2] = c
-    elif axis == 2:
-        R[:, 0, 0] = c
-        R[:, 0, 1] = s
-        R[:, 1, 0] = -s
-        R[:, 1, 1] = c
-
-    return R
-
-
-@torch.jit.script
-def euler_xyz_to_rotation_matrix(rpy):
-    N = rpy.size(0)
-    R = torch.tensor((N, 3, 3), dtype=torch.float, device=rpy.device)
-    R = torch.matmul(
-        torch.matmul(coordinate_rotation(0, rpy[:, 0]),
-                     coordinate_rotation(1, rpy[:, 1])),
-        coordinate_rotation(2, rpy[:, 2]))
-    return R.transpose(1, 2)
-
-
-def get_contact_normals(robots_feet_pos, mesh_normals, contact_forces):
-    pos_to_idx = robots_feet_pos[:, :, :2].to(torch.int)
-
-    pos_to_idx = torch.permute(pos_to_idx, (2, 0, 1))
-    pos_to_idx = torch.flatten(pos_to_idx, 1)
-    contact_normals = mesh_normals[pos_to_idx.tolist()]
-    contact_normals = contact_normals.view(-1, 4, 3)
-
-    filter = torch.norm(contact_forces[:, :, :], dim=2) > 1.
-    filter = torch.repeat_interleave(filter, repeats=3,
-                                     dim=1).view(-1, robots_feet_pos.shape[1],
-                                                 3)
-    contact_normals *= filter
-
-    return contact_normals.flatten(1)
-
-
-@torch.jit.script
-def vector_2_skewmat(v):
-    N = v.size(0)
-    a = torch.zeros((N, 3, 3), device=v.device, dtype=torch.float)
-    a[:, 2, 1] = v[:, 0]
-    a[:, 2, 0] = -v[:, 1]
-    a[:, 1, 0] = v[:, 2]
-    a -= a.clone().transpose(1, 2)
-    return a
-
-
-def parse_sim_params(args, cfg):
-    if 'real' in args.task:
-        return cfg
-    from isaacgym import gymapi, gymutil
-    # initialize sim params
-    sim_params = gymapi.SimParams()
-    if 'flex' in args.physics_engine:
-        args.physics_engine = gymapi.SIM_FLEX
-        if args.device != "cpu":
-            print("WARNING: Using Flex with GPU instead of PHYSX!")
-    else:
-        args.physics_engine = gymapi.SIM_PHYSX
-        sim_params.physx.use_gpu = True if 'cuda' in args.sim_device else False
-        sim_params.physx.num_subscenes = args.subscenes
-    sim_params.use_gpu_pipeline = True if 'cuda' in args.rl_device else False
-    # if sim options are provided in cfg, parse them and update/override above:
-    if "sim" in cfg:
-        gymutil.parse_sim_config(cfg["sim"], sim_params)
-    # Override num_threads if passed on the command line
-    if args.physics_engine == gymapi.SIM_PHYSX and args.num_threads > 0:
-        sim_params.physx.num_threads = args.num_threads
-
-    return sim_params