Using NVIDIA Isaac for Manipulation
Overview​
Robot manipulation is a complex field that involves planning and executing precise movements to interact with objects in the environment. NVIDIA Isaac provides powerful tools for simulating, developing, and testing manipulation tasks in a safe, controlled environment. This lesson explores how to leverage Isaac's capabilities for grasp planning, motion control, and AI-driven dexterity in robotic manipulation.
Manipulation in Robotics​
The Manipulation Pipeline​
Robot manipulation typically involves several interconnected components:
- Perception: Identifying and localizing objects to manipulate
- Planning: Determining how to approach and grasp objects
- Control: Executing precise movements to achieve manipulation goals
- Learning: Improving manipulation skills through experience
- Adaptation: Adjusting to changing conditions and unexpected situations
Challenges in Manipulation​
Manipulation tasks face several significant challenges:
- Contact Mechanics: Understanding and controlling physical interactions
- Precision Requirements: Achieving millimeter-level accuracy in some tasks
- Uncertainty Handling: Managing uncertainty in object poses and properties
- Dynamic Environments: Adapting to moving or changing objects
- Force Control: Managing forces during grasping and manipulation
- Multi-finger Coordination: Coordinating multiple fingers or end-effectors
Isaac's Manipulation Capabilities​
Physics Simulation for Manipulation​
Isaac provides realistic physics simulation essential for manipulation:
import omni
from omni.isaac.core import World
from omni.isaac.core.robots import Robot
from omni.isaac.core.articulations import Articulation
from omni.isaac.core.prims import RigidPrim, ClothPrim
import numpy as np
class ManipulationWorld(World):
def __init__(self):
super().__init__(stage_units_in_meters=1.0)
# Configure physics parameters for manipulation
self.physics_scene.set_solver_type("TGS") # Use TGS solver for stable contacts
self.physics_scene.set_friction_enabled(True)
self.physics_scene.set_rest_offset(0.001) # Small rest offset for stability
self.physics_scene.set_contact_offset(0.002) # Contact offset for proper contacts
# Add ground plane
self.scene.add_ground_plane(prim_path="/World/Ground",
static_friction=0.5,
dynamic_friction=0.5,
restitution=0.0)
# Add robot
self.robot = self.scene.add(
Robot(
prim_path="/World/Robot",
usd_path="/Isaac/Robots/Franka/franka_instanceable.usd",
position=np.array([0.0, 0.0, 0.0]),
orientation=np.array([0.0, 0.0, 0.0, 1.0])
)
)
# Add objects to manipulate
self.add_manipulation_objects()
def add_manipulation_objects(self):
"""Add objects for manipulation tasks"""
# Add a cube to manipulate
self.cube = self.scene.add(
RigidPrim(
prim_path="/World/Cube",
name="cube",
position=np.array([0.5, 0.0, 0.1]),
scale=np.array([0.05, 0.05, 0.05]),
mass=0.1,
collision_mesh_usd_path="cube.usd"
)
)
# Add a cylinder
self.cylinder = self.scene.add(
RigidPrim(
prim_path="/World/Cylinder",
name="cylinder",
position=np.array([0.6, 0.1, 0.1]),
scale=np.array([0.03, 0.03, 0.1]),
mass=0.05,
collision_mesh_usd_path="cylinder.usd"
)
)
# Initialize the manipulation world
world = ManipulationWorld()
world.reset()
Robot Configuration for Manipulation​
Configuring robots for manipulation tasks:
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.stage import add_reference_to_stage
from omni.isaac.manipulation import GraspPlanner
class ManipulationRobot:
def __init__(self, world):
self.world = world
self.robot = world.robot
self.grasp_planner = None
# Initialize robot controllers
self.setup_controllers()
def setup_controllers(self):
"""Set up robot controllers for manipulation"""
# Get robot's end-effector
self.end_effector_frame = self.robot.get_end_effector_frame()
# Initialize inverse kinematics solver
self.ik_solver = self.setup_inverse_kinematics()
# Initialize joint position controller
self.joint_controller = self.robot.get_articulation_controller()
# Initialize Cartesian controller
self.cartesian_controller = self.setup_cartesian_controller()
def setup_inverse_kinematics(self):
"""Set up inverse kinematics solver"""
from omni.isaac.core.utils.viewports import create_viewport
from omni.kit.primitive.mesh import SphereMesh
# Create IK solver for the robot arm
ik_solver = self.robot.create_ik_solver(
name="franka_ik",
joint_names=[
"panda_joint1", "panda_joint2", "panda_joint3",
"panda_joint4", "panda_joint5", "panda_joint6", "panda_joint7"
]
)
return ik_solver
def setup_cartesian_controller(self):
"""Set up Cartesian space controller"""
from omni.isaac.core.controllers import DifferentialIkController
cartesian_controller = DifferentialIkController(
name="cartesian_controller",
target_end_effector_name=self.end_effector_frame.name,
robot_articulation=self.robot,
joint_names=[
"panda_joint1", "panda_joint2", "panda_joint3",
"panda_joint4", "panda_joint5", "panda_joint6", "panda_joint7"
]
)
return cartesian_controller
# Example of setting up a manipulation robot
manipulation_robot = ManipulationRobot(world)
Grasp Planning in Isaac​
Physics-Based Grasp Simulation​
Isaac enables realistic grasp simulation with accurate physics:
from omni.isaac.core.utils.prims import get_prim_at_path
from pxr import Gf, UsdPhysics, PhysxSchema
import omni.physx
class GraspSimulator:
def __init__(self, robot, world):
self.robot = robot
self.world = world
self.physics_sim_view = world.physics_sim_view
# Initialize grasp analysis tools
self.contact_manager = self.setup_contact_analysis()
def setup_contact_analysis(self):
"""Set up contact analysis for grasp evaluation"""
# Enable contact reporting for the robot's gripper
gripper_links = self.get_gripper_links()
for link_path in gripper_links:
link_prim = get_prim_at_path(link_path)
# Enable contact reporting
UsdPhysics.CollisionAPI.Apply(link_prim)
# Set up contact filters if needed
contact_api = PhysxSchema.PhysxCollisionAPI.Apply(link_prim)
contact_api.GetContactOffsetAttr().Set(0.002)
contact_api.GetRestOffsetAttr().Set(0.001)
return self.physics_sim_view
def get_gripper_links(self):
"""Get all gripper link paths"""
# This would be specific to your robot's USD structure
gripper_paths = []
# Example for Franka Panda robot
if "franka" in self.robot.prim_path:
gripper_paths = [
f"{self.robot.prim_path}/panda_leftfinger",
f"{self.robot.prim_path}/panda_rightfinger",
f"{self.robot.prim_path}/panda_hand"
]
return gripper_paths
def evaluate_grasp_quality(self, object_prim_path, grasp_pose):
"""Evaluate the quality of a grasp using physics simulation"""
# Move robot to grasp pose
self.move_to_grasp_pose(grasp_pose)
# Apply small disturbance to test grasp stability
self.apply_disturbance(object_prim_path)
# Measure grasp stability over time
stability_score = self.measure_grasp_stability(object_prim_path)
return stability_score
def move_to_grasp_pose(self, grasp_pose):
"""Move robot to a specific grasp pose"""
# Use IK controller to move to grasp pose
joint_positions = self.inverse_kinematics(grasp_pose)
self.robot.set_joint_positions(joint_positions)
def inverse_kinematics(self, target_pose):
"""Calculate joint positions for target end-effector pose"""
# This would use the IK solver set up earlier
joint_positions = self.robot.ik_solver.compute(
target_position=target_pose.position,
target_orientation=target_pose.orientation
)
return joint_positions
def apply_disturbance(self, object_prim_path):
"""Apply small disturbance to test grasp stability"""
object_handle = self.physics_sim_view.get_rigid_body_view(object_prim_path)
# Apply small random force
disturbance_force = np.random.uniform(-0.5, 0.5, size=3)
object_handle.apply_force(disturbance_force, indices=[0])
def measure_grasp_stability(self, object_prim_path):
"""Measure how well the grasp holds the object"""
initial_position = self.get_object_position(object_prim_path)
# Run simulation for a short period
for _ in range(50): # 50 steps at 60Hz = ~0.83 seconds
self.world.step(render=False)
final_position = self.get_object_position(object_prim_path)
# Calculate displacement - less displacement = more stable grasp
displacement = np.linalg.norm(final_position - initial_position)
stability_score = 1.0 / (1.0 + displacement) # Higher score = more stable
return stability_score
def get_object_position(self, object_prim_path):
"""Get current position of object"""
object_handle = self.physics_sim_view.get_rigid_body_view(object_prim_path)
positions, orientations = object_handle.get_world_poses()
return positions[0]
AI-Based Grasp Planning​
Implementing AI-based grasp planning:
import torch
import torch.nn as nn
import numpy as np
class GraspPlannerNet(nn.Module):
def __init__(self, input_channels=3, num_classes=6): # 6-DOF grasp poses
super(GraspPlannerNet, self).__init__()
# Convolutional layers for processing depth/RGB images
self.conv_layers = nn.Sequential(
nn.Conv2d(input_channels, 32, kernel_size=5, stride=2),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=5, stride=2),
nn.ReLU(),
nn.Conv2d(64, 128, kernel_size=3, stride=2),
nn.ReLU(),
nn.Conv2d(128, 256, kernel_size=3, stride=2),
nn.ReLU()
)
# Calculate flattened size after convolutions
self.flattened_size = 256 * 4 * 4 # Assumes 64x64 input
# Fully connected layers
self.fc_layers = nn.Sequential(
nn.Linear(self.flattened_size, 512),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, num_classes)
)
def forward(self, x):
x = self.conv_layers(x)
x = x.view(-1, self.flattened_size)
x = self.fc_layers(x)
return x
class AIManipulationController:
def __init__(self, robot, camera):
self.robot = robot
self.camera = camera
# Initialize AI grasp planner
self.grasp_net = self.load_trained_model()
# Initialize grasp evaluation
self.grasp_evaluator = GraspSimulator(robot, robot.world)
def load_trained_model(self):
"""Load a pre-trained grasp planning model"""
# In practice, you'd load a model trained on Isaac-generated data
model = GraspPlannerNet()
# Load trained weights (example)
try:
model.load_state_dict(torch.load("grasp_planning_model.pth"))
model.eval()
print("Loaded trained grasp planning model")
except:
print("Could not load trained model, using random initialization")
return model
def plan_grasp_from_camera(self):
"""Plan a grasp based on camera observation"""
# Get camera image
camera_data = self.camera.get_current_frame()
rgb_image = camera_data["rgb"]
# Preprocess image for the network
input_tensor = self.preprocess_image(rgb_image)
# Get grasp predictions
with torch.no_grad():
grasp_predictions = self.grasp_net(input_tensor)
# Convert predictions to grasp poses
grasp_poses = self.predictions_to_grasps(grasp_predictions)
# Evaluate and select best grasp
best_grasp = self.select_best_grasp(grasp_poses)
return best_grasp
def preprocess_image(self, image):
"""Preprocess camera image for neural network"""
# Convert to tensor and normalize
image_tensor = torch.from_numpy(image).permute(2, 0, 1).float() / 255.0
# Resize to network input size (example: 64x64)
import torch.nn.functional as F
image_tensor = F.interpolate(
image_tensor.unsqueeze(0),
size=(64, 64),
mode='bilinear'
).squeeze(0)
return image_tensor.unsqueeze(0) # Add batch dimension
def predictions_to_grasps(self, predictions):
"""Convert network predictions to grasp poses"""
# This would convert network outputs to 6-DOF grasp poses
# In practice, this would be specific to your network architecture
# Example conversion (simplified)
grasp_poses = []
for i in range(predictions.shape[0]): # batch dimension
# Extract 6-DOF pose (x, y, z, rx, ry, rz)
pose_data = predictions[i].cpu().numpy()
grasp_pose = {
'position': pose_data[:3], # x, y, z
'orientation': pose_data[3:], # r, p, y or quaternion
'quality': self.calculate_grasp_quality(pose_data)
}
grasp_poses.append(grasp_pose)
return grasp_poses
def select_best_grasp(self, grasp_poses):
"""Select the best grasp from candidates"""
if not grasp_poses:
return None
# Evaluate each grasp using physics simulation
evaluated_grasps = []
for grasp in grasp_poses:
quality = self.grasp_evaluator.evaluate_grasp_quality(
object_prim_path="/World/Object", # Specify target object
grasp_pose=grasp
)
evaluated_grasps.append((grasp, quality))
# Sort by quality and return best
best_grasp = max(evaluated_grasps, key=lambda x: x[1])
return best_grasp[0] # Return just the grasp pose, not the quality
def execute_grasp(self, grasp_pose):
"""Execute a planned grasp"""
# Move to pre-grasp position
pre_grasp_pose = self.calculate_pre_grasp_pose(grasp_pose)
self.move_to_pose(pre_grasp_pose)
# Open gripper
self.open_gripper()
# Move to grasp pose
self.move_to_pose(grasp_pose)
# Close gripper
self.close_gripper()
# Lift object
lift_pose = self.calculate_lift_pose(grasp_pose)
self.move_to_pose(lift_pose)
def calculate_pre_grasp_pose(self, grasp_pose, distance=0.1):
"""Calculate pre-grasp pose (approach from above)"""
# Move slightly above the grasp pose
pre_grasp = grasp_pose.copy()
pre_grasp['position'][2] += distance # Lift up
return pre_grasp
def calculate_lift_pose(self, grasp_pose, lift_distance=0.1):
"""Calculate post-grasp lift pose"""
lift_pose = grasp_pose.copy()
lift_pose['position'][2] += lift_distance
return lift_pose
def move_to_pose(self, pose):
"""Move end-effector to specified pose"""
# Use Cartesian controller to move to pose
joint_positions = self.inverse_kinematics(pose)
self.robot.set_joint_positions(joint_positions)
def open_gripper(self):
"""Open the robot gripper"""
# Set gripper joint positions for open state
self.robot.set_gripper_positions([0.04, 0.04]) # Example for Franka
def close_gripper(self):
"""Close the robot gripper"""
# Set gripper joint positions for closed state
self.robot.set_gripper_positions([0.0, 0.0]) # Example for Franka
def inverse_kinematics(self, target_pose):
"""Calculate joint positions for target pose"""
# This would use the robot's IK solver
joint_positions = self.robot.ik_solver.compute(
target_position=target_pose['position'],
target_orientation=target_pose['orientation']
)
return joint_positions
Motion Control in Isaac​
Trajectory Planning and Execution​
Advanced motion control capabilities in Isaac:
from omni.isaac.core.utils.rotations import euler_angles_to_quat
import math
class MotionController:
def __init__(self, robot):
self.robot = robot
self.trajectory_generator = TrajectoryGenerator()
def plan_cartesian_trajectory(self, waypoints, max_velocity=0.5, max_acceleration=1.0):
"""Plan a Cartesian trajectory through waypoints"""
# Generate smooth trajectory between waypoints
trajectory = self.trajectory_generator.generate_cartesian_trajectory(
waypoints,
max_velocity,
max_acceleration
)
return trajectory
def execute_cartesian_trajectory(self, trajectory, controller_type="position"):
"""Execute a Cartesian trajectory"""
for waypoint in trajectory:
if controller_type == "position":
# Use position control
joint_positions = self.cartesian_to_joint(waypoint)
self.robot.set_joint_positions(joint_positions)
elif controller_type == "velocity":
# Use velocity control
joint_velocities = self.calculate_joint_velocities(waypoint)
self.robot.set_joint_velocities(joint_velocities)
# Step simulation
self.robot.world.step(render=True)
def cartesian_to_joint(self, cartesian_pose):
"""Convert Cartesian pose to joint positions using IK"""
joint_positions = self.robot.ik_solver.compute(
target_position=cartesian_pose['position'],
target_orientation=cartesian_pose['orientation']
)
return joint_positions
def calculate_joint_velocities(self, target_pose):
"""Calculate joint velocities to reach target pose"""
current_pose = self.get_current_end_effector_pose()
# Calculate Cartesian velocity
pos_error = target_pose['position'] - current_pose['position']
ori_error = target_pose['orientation'] - current_pose['orientation']
# Use Jacobian to convert Cartesian velocity to joint velocity
jacobian = self.compute_jacobian()
cartesian_velocity = np.concatenate([pos_error, ori_error])
joint_velocities = np.linalg.pinv(jacobian) @ cartesian_velocity
return joint_velocities
def compute_jacobian(self):
"""Compute geometric Jacobian for the robot"""
# This would use the robot's kinematic model
# In Isaac, you can use built-in methods or compute numerically
return self.robot.compute_jacobian()
def get_current_end_effector_pose(self):
"""Get current end-effector pose"""
ee_position, ee_orientation = self.robot.get_end_effector_position_orientation()
return {
'position': ee_position,
'orientation': ee_orientation
}
class TrajectoryGenerator:
def __init__(self):
self.interpolation_method = "cubic_spline"
def generate_cartesian_trajectory(self, waypoints, max_velocity, max_acceleration):
"""Generate smooth Cartesian trajectory"""
if len(waypoints) < 2:
return waypoints
# Use cubic spline interpolation for smooth trajectories
if self.interpolation_method == "cubic_spline":
return self.cubic_spline_interpolation(waypoints, max_velocity, max_acceleration)
else:
return self.linear_interpolation(waypoints)
def cubic_spline_interpolation(self, waypoints, max_velocity, max_acceleration):
"""Generate cubic spline trajectory through waypoints"""
import scipy.interpolate as spi
# Extract positions for interpolation
positions = np.array([wp['position'] for wp in waypoints])
# Parameterize path
distances = [0]
for i in range(1, len(positions)):
dist = np.linalg.norm(positions[i] - positions[i-1])
distances.append(distances[-1] + dist)
# Create spline interpolators for each dimension
tck_x = spi.splprep([positions[:, 0]], u=distances, s=0)[0]
tck_y = spi.splprep([positions[:, 1]], u=distances, s=0)[0]
tck_z = spi.splprep([positions[:, 2]], u=distances, s=0)[0]
# Generate intermediate points
total_distance = distances[-1]
num_points = max(int(total_distance * 10), 10) # 10 points per unit distance
u_new = np.linspace(0, total_distance, num_points)
# Interpolate positions
x_new = spi.splev(u_new, tck_x)
y_new = spi.splev(u_new, tck_y)
z_new = spi.splev(u_new, tck_z)
# Create interpolated waypoints
interpolated_waypoints = []
for i in range(len(u_new)):
waypoint = {
'position': np.array([x_new[i], y_new[i], z_new[i]]),
'orientation': waypoints[0]['orientation'] # Keep initial orientation
}
interpolated_waypoints.append(waypoint)
return interpolated_waypoints
def linear_interpolation(self, waypoints):
"""Simple linear interpolation between waypoints"""
interpolated = []
for i in range(len(waypoints) - 1):
start_wp = waypoints[i]
end_wp = waypoints[i + 1]
# Calculate distance between waypoints
distance = np.linalg.norm(end_wp['position'] - start_wp['position'])
num_interpolations = max(int(distance * 10), 5) # 10 points per unit
for j in range(num_interpolations):
t = j / num_interpolations
interp_pos = (1 - t) * start_wp['position'] + t * end_wp['position']
interpolated.append({
'position': interp_pos,
'orientation': start_wp['orientation']
})
return interpolated + [waypoints[-1]] # Add final waypoint
Force Control and Impedance Control​
Advanced control techniques for manipulation:
class ForceImpedanceController:
def __init__(self, robot, stiffness=1000, damping=20):
self.robot = robot
self.stiffness = stiffness
self.damping = damping
# Initialize force/torque sensors
self.setup_force_sensors()
def setup_force_sensors(self):
"""Set up force/torque sensors for the robot"""
# In Isaac Sim, force sensors can be added to robot links
from omni.isaac.core.sensors import ContactSensor
# Add contact sensors to gripper fingers
self.left_finger_sensor = ContactSensor(
prim_path=f"{self.robot.prim_path}/panda_leftfinger/ContactSensor",
translation=np.array([0.0, 0.0, 0.05]),
radius=0.01
)
self.right_finger_sensor = ContactSensor(
prim_path=f"{self.robot.prim_path}/panda_rightfinger/ContactSensor",
translation=np.array([0.0, 0.0, 0.05]),
radius=0.01
)
def impedance_control(self, desired_pose, desired_stiffness=None):
"""Implement impedance control for compliant manipulation"""
if desired_stiffness is not None:
stiffness = desired_stiffness
else:
stiffness = self.stiffness
current_pose = self.get_current_end_effector_pose()
# Calculate position and orientation errors
pos_error = desired_pose['position'] - current_pose['position']
ori_error = self.orientation_error(desired_pose['orientation'],
current_pose['orientation'])
# Calculate desired forces based on stiffness
force = stiffness * pos_error
torque = stiffness * ori_error
# Apply impedance control law
current_vel = self.get_end_effector_velocity()
damping_force = self.damping * current_vel[:3] # Linear damping
damping_torque = self.damping * current_vel[3:] # Angular damping
# Total control effort
total_force = force - damping_force
total_torque = torque - damping_torque
# Convert forces to joint torques using transpose Jacobian
jacobian = self.compute_jacobian()
jacobian_transpose = jacobian.T
joint_torques = np.concatenate([total_force, total_torque]) @ jacobian_transpose
# Apply torques to robot joints
self.robot.set_joint_efforts(joint_torques)
def orientation_error(self, desired_quat, current_quat):
"""Calculate orientation error as a 3D vector"""
# Convert quaternions to rotation matrices
from scipy.spatial.transform import Rotation as R
desired_rot = R.from_quat(desired_quat)
current_rot = R.from_quat(current_quat)
# Calculate relative rotation
relative_rot = desired_rot * current_rot.inv()
# Convert to axis-angle representation
axis_angle = relative_rot.as_rotvec()
return axis_angle
def get_end_effector_velocity(self):
"""Get current end-effector velocity"""
# This would use the robot's kinematic model and joint velocities
joint_velocities = self.robot.get_joint_velocities()
# Convert joint velocities to Cartesian velocities using Jacobian
jacobian = self.compute_jacobian()
cartesian_vel = jacobian @ joint_velocities
return cartesian_vel
def hybrid_force_position_control(self, position_targets, force_targets, selection_mask):
"""Implement hybrid force-position control"""
# selection_mask indicates which DOFs are position-controlled (1) vs force-controlled (0)
# 6 DOFs: [x, y, z, rx, ry, rz]
current_pose = self.get_current_end_effector_pose()
current_wrench = self.get_end_effector_wrench()
# Calculate control commands for each DOF
control_commands = np.zeros(6)
for i in range(6):
if selection_mask[i] == 1: # Position control
if i < 3: # Linear DOF
pos_error = position_targets[i] - current_pose['position'][i]
control_commands[i] = self.stiffness * pos_error
else: # Angular DOF
# Orientation control would be implemented here
control_commands[i] = 0
else: # Force control
force_error = force_targets[i] - current_wrench[i]
control_commands[i] = force_error # Pure force control
# Apply control commands
jacobian_transpose = self.compute_jacobian().T
joint_torques = control_commands @ jacobian_transpose
self.robot.set_joint_efforts(joint_torques)
def get_end_effector_wrench(self):
"""Get current end-effector wrench (force and torque)"""
# Get contact forces from sensors
left_force = self.left_finger_sensor.get_contact_force()
right_force = self.right_finger_sensor.get_contact_force()
# Combine forces (simplified)
total_force = left_force + right_force
# In practice, you'd also calculate torques
total_wrench = np.concatenate([total_force, np.zeros(3)]) # [fx, fy, fz, tx, ty, tz]
return total_wrench
AI in Dexterity​
Learning-Based Manipulation​
Using reinforcement learning for dexterous manipulation:
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
class ManipulationPolicyNetwork(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim=256):
super(ManipulationPolicyNetwork, self).__init__()
# Actor network (policy)
self.actor = nn.Sequential(
nn.Linear(state_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, action_dim),
nn.Tanh() # Actions in [-1, 1]
)
# Critic network (value function)
self.critic = nn.Sequential(
nn.Linear(state_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1) # Value output
)
# Action scaling (to map [-1, 1] to actual action space)
self.register_buffer('action_scale', torch.ones(action_dim))
self.register_buffer('action_bias', torch.zeros(action_dim))
def forward(self, state):
action = self.actor(state)
value = self.critic(state)
return action * self.action_scale + self.action_bias, value
def get_action(self, state):
"""Get action from policy (without gradients)"""
with torch.no_grad():
action, _ = self.forward(state)
return action.numpy()
def get_value(self, state):
"""Get value estimation (without gradients)"""
with torch.no_grad():
_, value = self.forward(state)
return value.item()
class ManipulationRLAgent:
def __init__(self, state_dim, action_dim, lr=3e-4):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Initialize networks
self.policy_net = ManipulationPolicyNetwork(state_dim, action_dim).to(self.device)
self.target_net = ManipulationPolicyNetwork(state_dim, action_dim).to(self.device)
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=lr)
# Experience replay buffer
self.replay_buffer = []
self.buffer_capacity = 100000
self.batch_size = 128
# Training parameters
self.gamma = 0.99 # Discount factor
self.tau = 0.005 # Soft update parameter
self.update_every = 4 # Update frequency
def select_action(self, state, add_noise=True):
"""Select action using the policy"""
state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
action, _ = self.policy_net(state_tensor)
if add_noise:
# Add exploration noise
noise = torch.randn_like(action) * 0.1
action = torch.clamp(action + noise, -1, 1)
return action.cpu().numpy()[0]
def train(self, experiences):
"""Train the policy on a batch of experiences"""
if len(experiences) < self.batch_size:
return
# Sample batch from replay buffer
batch = self.sample_batch(experiences)
states = torch.FloatTensor(batch['states']).to(self.device)
actions = torch.FloatTensor(batch['actions']).to(self.device)
rewards = torch.FloatTensor(batch['rewards']).unsqueeze(1).to(self.device)
next_states = torch.FloatTensor(batch['next_states']).to(self.device)
dones = torch.BoolTensor(batch['dones']).unsqueeze(1).to(self.device)
# Compute target Q-values
with torch.no_grad():
next_actions, _ = self.target_net(next_states)
next_q_values = self.target_net.critic(next_states)
target_q_values = rewards + (self.gamma * next_q_values * ~dones)
# Compute current Q-values
current_q_values = self.policy_net.critic(states)
# Compute critic loss
critic_loss = nn.MSELoss()(current_q_values, target_q_values)
# Compute actor loss (maximize Q-values)
predicted_actions, _ = self.policy_net(states)
actor_loss = -self.policy_net.critic(
torch.cat([states[:, :-action_dim], predicted_actions], dim=1)
).mean()
# Update networks
self.optimizer.zero_grad()
(critic_loss + actor_loss).backward()
self.optimizer.step()
# Soft update target network
self.soft_update()
return actor_loss.item(), critic_loss.item()
def sample_batch(self, experiences):
"""Sample a batch of experiences from replay buffer"""
indices = np.random.choice(len(experiences), self.batch_size, replace=False)
batch = {
'states': [experiences[i][0] for i in indices],
'actions': [experiences[i][1] for i in indices],
'rewards': [experiences[i][2] for i in indices],
'next_states': [experiences[i][3] for i in indices],
'dones': [experiences[i][4] for i in indices]
}
return batch
def soft_update(self):
"""Soft update target network parameters"""
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data)
class IsaacManipulationEnvironment:
def __init__(self, robot, camera, objects):
self.robot = robot
self.camera = camera
self.objects = objects
# Define action and observation spaces
self.action_dim = 7 # Joint position commands for 7-DOF arm
self.state_dim = 30 # Example: joint positions, velocities, object poses, etc.
# Initialize RL agent
self.agent = ManipulationRLAgent(self.state_dim, self.action_dim)
def get_state(self):
"""Get current state for RL agent"""
# Combine various sensor readings into state vector
joint_positions = self.robot.get_joint_positions()
joint_velocities = self.robot.get_joint_velocities()
# Get object poses relative to robot
object_poses = []
for obj in self.objects:
obj_pose = self.get_relative_pose(obj)
object_poses.extend([obj_pose[0], obj_pose[1], obj_pose[2]]) # Just position
# Get end-effector pose
ee_pose = self.robot.get_end_effector_position_orientation()
# Combine into state vector
state = np.concatenate([
joint_positions,
joint_velocities,
object_poses,
ee_pose[0], # Position
ee_pose[1] # Orientation
])
return state
def get_relative_pose(self, object_prim):
"""Get object pose relative to robot base"""
obj_pos, obj_ori = object_prim.get_world_poses()
base_pos, base_ori = self.robot.get_world_poses()
# Calculate relative position
relative_pos = obj_pos - base_pos
return relative_pos
def compute_reward(self, action):
"""Compute reward for manipulation task"""
# Example reward function for reaching and grasping
ee_pos = self.robot.get_end_effector_position()
obj_pos = self.objects[0].get_world_poses()[0] # Target object
# Distance to object
dist_to_obj = np.linalg.norm(ee_pos - obj_pos)
# Reward shaping
reward = 0
# Reach reward - encourage getting closer to object
reward -= dist_to_obj * 0.1
# Bonus for getting very close
if dist_to_obj < 0.05:
reward += 1.0
# Check if grasping is successful
if self.is_grasp_successful():
reward += 10.0 # Large bonus for successful grasp
# Penalty for large actions (smooth movement)
reward -= np.sum(np.square(action)) * 0.001
return reward
def is_grasp_successful(self):
"""Check if grasp is successful"""
# This would check contact sensors, object motion, etc.
# Simplified example:
left_contact = self.check_contact("left_finger")
right_contact = self.check_contact("right_finger")
object_moving = self.is_object_moving()
return left_contact and right_contact and not object_moving
def check_contact(self, finger_name):
"""Check if finger is in contact"""
# This would use contact sensor data
return True # Simplified
def is_object_moving(self):
"""Check if object is moving after grasp"""
# This would check object velocity
return False # Simplified
def reset(self):
"""Reset environment to initial state"""
# Reset robot to initial configuration
initial_joints = [0.0, -0.785, 0.0, -2.356, 0.0, 1.571, 0.785] # Example
self.robot.set_joint_positions(initial_joints)
# Reset object positions
for i, obj in enumerate(self.objects):
obj.set_world_poses(
positions=np.array([[0.5 + i*0.1, 0.0, 0.1]]),
orientations=np.array([[0.0, 0.0, 0.0, 1.0]])
)
# Return initial state
return self.get_state()
def step(self, action):
"""Execute action and return transition"""
# Apply action to robot
self.robot.set_joint_positions(action)
# Step simulation
self.robot.world.step(render=True)
# Get next state
next_state = self.get_state()
# Compute reward
reward = self.compute_reward(action)
# Check if episode is done
done = self.is_episode_done()
# Additional info
info = {
'success': self.is_grasp_successful(),
'distance': np.linalg.norm(
self.robot.get_end_effector_position() -
self.objects[0].get_world_poses()[0]
)
}
return next_state, reward, done, info
def is_episode_done(self):
"""Check if episode is done"""
# Episode could end if:
# - Successful task completion
# - Max steps reached
# - Robot violates constraints
return False # Simplified
Practical Implementation Example​
Complete Manipulation Task​
Here's a complete example of implementing a pick-and-place task:
def run_manipulation_demo():
"""Run a complete manipulation demonstration"""
# Initialize Isaac environment
from omni.isaac.core import World
from omni.isaac.core.robots import Robot
from omni.isaac.core.prims import RigidPrim
# Create world
world = World(stage_units_in_meters=1.0)
world.scene.add_ground_plane("/World/Ground", static_friction=0.5, dynamic_friction=0.5)
# Add robot
robot = world.scene.add(
Robot(
prim_path="/World/Robot",
usd_path="/Isaac/Robots/Franka/franka_instanceable.usd",
position=np.array([0.0, 0.0, 0.0]),
orientation=np.array([0.0, 0.0, 0.0, 1.0])
)
)
# Add objects to manipulate
cube = world.scene.add(
RigidPrim(
prim_path="/World/Cube",
name="cube",
position=np.array([0.5, 0.0, 0.1]),
scale=np.array([0.05, 0.05, 0.05]),
mass=0.1
)
)
# Add target location
target = world.scene.add(
RigidPrim(
prim_path="/World/Target",
name="target",
position=np.array([0.7, 0.3, 0.1]),
scale=np.array([0.1, 0.1, 0.01]),
mass=0.0
)
)
# Reset world
world.reset()
# Initialize manipulation controller
manipulator = AIManipulationController(robot)
motion_controller = MotionController(robot)
# Perform pick and place task
print("Starting pick and place task...")
# 1. Plan grasp for the cube
cube_position = cube.get_world_poses()[0]
grasp_pose = {
'position': cube_position + np.array([0, 0, 0.1]), # Above cube
'orientation': np.array([0, 0, 0, 1])
}
print("Executing grasp...")
manipulator.execute_grasp(grasp_pose)
# 2. Move to target location
target_position = target.get_world_poses()[0]
place_pose = {
'position': target_position + np.array([0, 0, 0.2]), # Above target
'orientation': np.array([0, 0, 0, 1])
}
print("Moving to target...")
motion_controller.move_to_pose(place_pose)
# 3. Release object
print("Releasing object...")
manipulator.open_gripper()
print("Pick and place task completed!")
# Wait for a moment to observe result
for _ in range(100):
world.step(render=True)
# Run the demonstration
if __name__ == "__main__":
run_manipulation_demo()
Best Practices for Isaac Manipulation​
1. Physics Parameter Tuning​
- Use appropriate friction and restitution values for objects
- Tune solver parameters for stable contact simulation
- Set proper collision margins for accurate contact detection
2. Control Strategy Selection​
- Use position control for precision tasks
- Use force control for compliant tasks
- Use hybrid force-position control for contact-rich tasks
3. Sensor Integration​
- Combine multiple sensor modalities for robust perception
- Use realistic sensor noise models
- Implement sensor fusion techniques
4. Learning Approaches​
- Use simulation-to-real transfer techniques
- Implement curriculum learning for complex tasks
- Use domain randomization for robust policies
Learning Objectives​
By the end of this lesson, you should be able to:
- Configure robots in Isaac for manipulation tasks with appropriate physics parameters
- Implement grasp planning using both physics simulation and AI techniques
- Design and execute motion control strategies for manipulation
- Apply force and impedance control for compliant manipulation
- Implement learning-based approaches for dexterous manipulation
- Evaluate manipulation performance and optimize control strategies
- Integrate perception and control for complete manipulation pipelines
- Understand the trade-offs between different manipulation control approaches