AI for Medical Robotics and Autonomous Systems

Module: Healthcare AI | Difficulty: Advanced

Robot Kinematics

Path Planning Cost

Force Control

Medical Robotics AI Tasks

| Task | Modality | Accuracy | Safety Requirement | |------|----------|----------|-------------------| | Suturing | Vision + Force | 0.94 | Critical | | Needle Driving | Vision | 0.91 | High | | Tissue Retraction | Force + Vision | 0.88 | High | | Instrument Navigation | 3D Vision | 0.93 | Critical | | Collision Avoidance | Multi-modal | 0.97 | Critical |

import torch
import torch.nn as nn

class SurgicalRobotController(nn.Module):
    def __init__(self, state_dim=12, action_dim=6):
        super().__init__()
        self.state_encoder = nn.Sequential(
            nn.Linear(state_dim, 128), nn.ReLU(),
            nn.Linear(128, 64), nn.ReLU())
        self.action_encoder = nn.Sequential(
            nn.Linear(action_dim, 64), nn.ReLU())
        self.joint = nn.Sequential(
            nn.Linear(128, 64), nn.ReLU(),
            nn.Linear(64, action_dim))

    def forward(self, state, previous_action=None):
        state_feat = self.state_encoder(state)
        if previous_action is not None:
            action_feat = self.action_encoder(previous_action)
            combined = torch.cat([state_feat, action_feat], dim=1)
        else:
            combined = state_feat
        return self.joint(combined)

class DepthEstimator(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = models.resnet18(pretrained=True)
        num_features = self.encoder.fc.in_features
        self.encoder.fc = nn.Identity()
        self.depth_head = nn.Sequential(
            nn.Linear(num_features, 512), nn.ReLU(),
            nn.Linear(512, 224 * 224))

    def forward(self, x):
        features = self.encoder(x)
        depth = self.depth_head(features)
        return depth.reshape(-1, 1, 224, 224)

robot_controller = SurgicalRobotController(state_dim=12, action_dim=6)
state = torch.randn(1, 12)
action = robot_controller(state)
print(f'Robot action: {action.shape}')

depth_model = DepthEstimator()
rgb = torch.randn(1, 3, 224, 224)
depth = depth_model(rgb)
print(f'Depth estimation: {depth.shape}')

Research Insight: Autonomous surgical robots require real-time perception, planning, and control with guaranteed safety. The key challenge is handling unexpected situations: tissue deformation, bleeding, or anatomical variations that were not seen during training. Reinforcement learning with safety constraints (Constrained MDPs) is emerging as the most promising approach for learning surgical skills while ensuring patient safety.