A hands-on tutorial for the OpenClaw Python SDK. Covers installation, connecting to a robot (or simulator), writing pick-and-place scripts, handling errors, and integrating with popular AI libraries like OpenCV and PyTorch for vision-guided manipulation.
Run pip install openclaw in your Python 3.10+ environment. For vision-guided manipulation extras: pip install openclaw[vision] which includes OpenCV and numpy bindings. For full AI integration: pip install openclaw[ai] which adds PyTorch and torchvision support.
Import the SDK and create a robot instance: from openclaw import Robot; robot = Robot(model="ur5e", ip="192.168.1.100"). For simulation use: robot = Robot(model="ur5e", sim=True). Use the context manager pattern: async with robot.session() as r: await r.move_joints([0, -1.57, 1.57, -1.57, -1.57, 0]).
Use the high-level Cartesian API: await robot.move_to(x=0.5, y=0.0, z=0.4); await robot.gripper.open(); await robot.move_to(x=0.5, y=0.0, z=0.2); await robot.gripper.close(); await robot.move_to(x=0.5, y=0.0, z=0.4). The SDK handles inverse kinematics and collision avoidance automatically.
Yes. Install openclaw[vision], then use the built-in VisionPipeline class which integrates with OpenCV. The pipeline accepts a camera frame, runs object detection, and returns Cartesian pick coordinates that can be passed directly to robot.move_to(). Example: pick_pose = await vision.detect_object(frame, class_id="red_cube"); await robot.move_to(**pick_pose).
OpenClaw raises typed exceptions: openclaw.CollisionError, openclaw.JointLimitError, openclaw.ConnectionError, and openclaw.EmergencyStopError. Wrap robot commands in try/except blocks and always call await robot.recover() after a recoverable error before resuming motion. The context manager automatically calls robot.safe_stop() on any unhandled exception.
Integrate OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, Google Gemini 2.0 Flash, and self-hosted Ollama LLaMA 3 with OpenClaw for natural-language robot instruction and AI-powered manipulation planning. Includes detailed per-call and monthly cost breakdowns for each AI provider.
Deploy OpenClaw on AWS EC2 and EKS, Azure Virtual Machines and AKS, and Google Cloud Compute Engine and GKE. Includes detailed cost breakdowns per cloud, GPU instance sizing for simulation, and architecture patterns for hybrid cloud-to-robot connectivity.
Deploy OpenClaw inside Docker containers for reproducible, portable robotics development. This guide covers single-container setups, multi-service docker-compose stacks, GPU passthrough for simulation, and production image hardening.