Perception is the foundation of intelligent robotics — robots must understand their environment to act effectively. AI has transformed robot perception from simple sensor processing to rich scene understanding.
Computer Vision for Robots
Visual perception enables robots to understand their surroundings:
- Object Detection & Recognition — identify and classify objects in the workspace
- Semantic Segmentation — understand every pixel (floor, wall, obstacle, person)
- Depth Estimation — perceive 3D structure from 2D cameras (monocular depth)
- Visual SLAM — simultaneous localization and mapping from camera feeds
- 6DOF Pose Estimation — determine exact position and orientation of objects for grasping
3D Perception
Beyond 2D images to full 3D understanding:
- LiDAR Processing — PointNet and related architectures process point clouds
- RGB-D Fusion — combine color cameras with depth sensors for rich 3D models
- Occupancy Networks — neural representations of 3D space
- NeRF for Robotics — neural radiance fields for novel view synthesis and scene understanding
Tactile & Force Sensing
AI processes touch feedback for dexterous manipulation:
- Grasp Stability — predict whether a grasp will succeed from tactile sensor data
- Object Properties — estimate hardness, texture, and weight from touch
- Contact Prediction — anticipate contact forces during manipulation
- GelSight — camera-based tactile sensors with AI processing for detailed surface analysis
Multi-Modal Perception
Robots combine multiple sensors for robust understanding:
- Sensor Fusion — combine vision, LiDAR, radar, IMU, and touch data
- Cross-Modal Learning — learn correspondences between vision and touch, or vision and audio
- Foundation Models for Robots — large pre-trained models adapted for robotic perception
- Open-Vocabulary Detection — recognize objects from natural language descriptions using CLIP-based models