AI Notes
Learn AI for robotics. Robot learning, autonomy. AI robotics 2024.
Introduction
AI in robotics represents the convergence of two transformative fields: artificial intelligence provides the "brain" (perception, planning, decision-making, learning) while robotics provides the "body" (sensors, actuators, physical interaction with the world). Together, they create autonomous systems capable of perceiving their environment, reasoning about actions, and executing physical tasks without constant human supervision.
Modern AI-powered robots operate in warehouses (Amazon's Kiva robots), perform surgery (da Vinci surgical system), explore other planets (Mars rovers), drive vehicles (Waymo), and assist in homes (robotic vacuums). The integration of deep learning with robotic systems has accelerated capabilities dramatically since 2015.
The Sense-Plan-Act Paradigm
The classical robotics architecture decomposes intelligence into three phases:
| SENSE | PLAN → ACT |
| Cameras | Object detection, scene understanding |
| LiDAR | 3D point clouds, depth mapping |
| IMU | Orientation, acceleration |
| Touch sensors | Contact forces, grasping feedback |
| Microphones | Voice commands, sound localization |
| Path planning | A*, RRT, PRM algorithms |
| Task planning | Hierarchical task decomposition |
| Motion planning | Trajectory optimization |
| Contingency planning | What-if scenarios |
| Motor control | PID controllers, torque commands |
| Grasping | Grip force regulation |
| Locomotion | Walking, rolling, flying |
| Manipulation | Pick-and-place, assembly |
AI Techniques in Robotics
Computer Vision for Robots
| Object Recognition | "What is this thing?" |
| Pose Estimation | "Where is it and how is it oriented?" |
| Visual SLAM | "Where am I? What does the world look like?" |
| Depth Estimation | "How far away is everything?" |
Reinforcement Learning for Robots
Natural Language for Robot Interaction
| Voice commands | intent parsing → action execution |
| Parse | action=pick_up, object=cup, color=red, location=table |
| Ground | identify red cup in visual scene |
| Plan | reach trajectory to cup |
| Execute | grasp and lift |
| Modern | Large Language Models as robot planners |
| GPT-4 | break complex tasks into subtask sequences |
| "Clean the kitchen" | [clear_counter, wash_dishes, wipe_surfaces, sweep_floor] |
Robot Architectures
Reactive Architecture (Brooks' Subsumption)
| Direct sensor | actuator mappings |
| Level 3: [Explore] | wander randomly |
| Level 2: [Avoid obstacles] | turn away from objects |
| Level 1: [Emergency stop] | halt if collision imminent |
Deliberative Architecture
| Sense | Build model → Plan → Execute |
| Strengths | optimal plans, handles complex goals |
| Weaknesses | slow (planning takes time), brittle models |
Hybrid Architecture (Most Common)
| Reactive layer | fast reflexes (obstacle avoidance) |
| Deliberative layer | strategic planning (path to goal) |
| Executive layer | coordinates between layers |
| Example | Self-driving car |
| Reactive | Emergency braking (< 10ms response) |
| Planning | Route planning, lane changes (100ms-1s) |
| Strategic | Trip planning, fuel/charge management (minutes) |
Key Challenges
| 1. Uncertainty | Real world is noisy and unpredictable |
| Solution | Probabilistic methods, robust planning |
| 2. Real-time constraints | Must respond in milliseconds |
| Solution | Hierarchical control, anytime algorithms |
| 3. Safety | Robots interact with humans physically |
| Solution | Safety constraints, human-aware planning |
| 4. Generalization | New environments, new objects |
| Solution | Domain randomization, foundation models |
| 5. Long-horizon tasks | Multi-step, hours-long tasks |
| Solution | Hierarchical planning, LLM-based task decomposition |
Current State-of-the-Art
| Application | Key Technology | Example System |
|---|---|---|
| Warehouse logistics | Navigation + manipulation | Amazon Sparrow |
| Surgery | Precision control + vision | da Vinci Xi |
| Self-driving | Perception + planning | Waymo Driver |
| Agriculture | Vision + manipulation | Agrobot harvester |
| Space exploration | Autonomous navigation | Mars Perseverance |
| Home assistance | NLP + manipulation | Figure 01, Tesla Optimus |
Interview Questions
Q: What is the sim-to-real gap and how do you address it? A: Simulation physics differs from reality (friction, deformation, sensor noise). Policies trained purely in simulation often fail on real robots. Solutions: domain randomization (train with varied physics parameters so policy becomes robust), system identification (calibrate simulator to match reality), and progressive fine-tuning on real hardware with small amounts of real data.
Q: Why is manipulation harder than navigation for robots? A: Navigation operates in 2D/3D free space with relatively simple physics. Manipulation involves contact dynamics (grasping forces, friction, deformation), high-dimensional action spaces (multiple joint angles), and object diversity (every object has different shape, weight, and material properties). A navigation algorithm works for any floor; a grasp strategy may fail for each new object.
Q: How do modern robots handle unknown objects? A: Through category-level generalization: train on diverse objects so the system learns general grasping strategies (approach from above, pinch thin objects, envelope large ones). Foundation models (like RT-2 from Google) trained on internet-scale data can generalize to objects never seen during robot training by leveraging semantic understanding from vision-language models.
Exam Focus
Revise definitions, diagrams, examples, and short-answer points for AI in Robotics - Intelligent Automation.
Interview Use
Prepare one clear explanation, one practical example, and one common mistake for this Artificial Intelligence topic.
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