Robots

Strands Robots is a Python library for controlling physical robots with natural language. It provides a policy abstraction layer for vision-language-action (VLA) models and a hardware abstraction layer for robot control, letting you tell a robot what to do without programming it.

The library provides a set of Strands Agents tools that handle several components of the robotics stack - from camera capture and servo calibration to policy inference and real-time control loops. An agent equipped with these tools can interpret instructions like “pick up the red block” and translate them into coordinated motor actions.

Getting started

Installation

pip install strands-robots

Basic usage

from strands import Agent
from strands_robots import Robot, gr00t_inference

robot = Robot(
    tool_name="my_arm",
    robot="so101_follower",
    cameras={
        "front": {"type": "opencv", "index_or_path": "/dev/video0", "fps": 30},
        "wrist": {"type": "opencv", "index_or_path": "/dev/video2", "fps": 30},
    },
    port="/dev/ttyACM0",
    data_config="so100_dualcam",
)

agent = Agent(tools=[robot, gr00t_inference])

# Start the inference service
agent.tool.gr00t_inference(
    action="start",
    checkpoint_path="/data/checkpoints/model",
    port=5555,
    data_config="so100_dualcam",
)

# Control the robot with natural language
agent("Use my_arm to pick up the red block using GR00T policy on port 5555")

The Robot class is a Strands AgentTool that the agent can invoke directly. When the agent decides to use the robot, it calls the tool with an instruction and policy port, and the tool handles the entire observation-inference-action loop internally.

How it works

The system chains together three layers: a Strands Agent that interprets natural language, a policy provider that maps camera observations and instructions to action chunks, and a hardware abstraction layer that sends those actions to physical actuators.

graph LR
    A[Natural Language<br/>'Pick up the red block'] --> B[Strands Agent]
    B --> C[Robot class]
    C --> D[Policy Provider]
    C --> E[Hardware Abstraction]
    D --> F[Action Chunk]
    F --> E
    E --> G[Robot Hardware]

    classDef input fill:#2ea44f,stroke:#1b7735,color:#fff
    classDef agent fill:#0969da,stroke:#044289,color:#fff
    classDef policy fill:#8250df,stroke:#5a32a3,color:#fff
    classDef hardware fill:#bf8700,stroke:#875e00,color:#fff

    class A input
    class B,C agent
    class D,F policy
    class E,G hardware

Each control cycle, the Robot class captures observations (camera frames and joint states), sends them to the policy for inference, receives an action chunk, and executes those actions on the hardware.

Architecture

flowchart TB
    subgraph Agent["🤖 Strands Agent"]
        NL[Natural Language Input]
        Tools[Tool Registry]
    end

    subgraph RobotTool["🦾 Robot Class"]
        direction TB
        RT[Robot Class]
        TM[Task Manager]
        AS[Async Executor]
    end

    subgraph Policy["🧠 Policy Layer"]
        direction TB
        PA[Policy Abstraction]
        GP[GR00T Policy]
        MP[Mock Policy]
        CP[Custom Policy]
    end

    subgraph Inference["⚡ Inference Service"]
        direction TB
        DC[Docker Container]
        ZMQ[ZMQ Server :5555]
        TRT[TensorRT Engine]
    end

    subgraph Hardware["🔧 Hardware Layer"]
        direction TB
        LR[LeRobot]
        CAM[Cameras]
        SERVO[Feetech Servos]
    end

    NL --> Tools
    Tools --> RT
    RT --> TM
    TM --> AS
    AS --> PA
    PA --> GP
    PA --> MP
    PA --> CP
    GP --> ZMQ
    ZMQ --> TRT
    TRT --> DC
    AS --> LR
    LR --> CAM
    LR --> SERVO

    classDef agentStyle fill:#0969da,stroke:#044289,color:#fff
    classDef robotStyle fill:#2ea44f,stroke:#1b7735,color:#fff
    classDef policyStyle fill:#8250df,stroke:#5a32a3,color:#fff
    classDef infraStyle fill:#bf8700,stroke:#875e00,color:#fff
    classDef hwStyle fill:#d73a49,stroke:#a72b3a,color:#fff

    class NL,Tools agentStyle
    class RT,TM,AS robotStyle
    class PA,GP,MP,CP policyStyle
    class DC,ZMQ,TRT infraStyle
    class LR,CAM,SERVO hwStyle

Control flow

sequenceDiagram
    participant User
    participant Agent as Strands Agent
    participant Robot as Robot Class
    participant Policy as Policy Provider
    participant HW as Hardware

    User->>Agent: "Pick up the red block"
    Agent->>Robot: execute(instruction, policy_port)

    loop Control Loop
        Robot->>HW: get_observation()
        HW-->>Robot: {cameras, joint_states}
        Robot->>Policy: get_actions(obs, instruction)
        Policy-->>Robot: action_chunk

        loop Action Horizon
            Robot->>HW: send_action(action)
            Note over Robot,HW: sleep
        end
    end

    Robot-->>Agent: Task completed
    Agent-->>User: "Picked up red block"

Core concepts

Robot class

The Robot class wraps a robot and exposes it as a Strands agent tool with four actions:

Action	Behavior	Use case
execute	Blocks until the task completes or times out	Single-step tasks
start	Returns immediately, runs task in background	Long-running tasks
status	Reports current task progress	Monitoring async tasks
stop	Interrupts a running task	Emergency stop

# Blocking - agent waits for completion
agent("Use my_arm to pick up the red block using GR00T policy on port 5555")

# Async - agent can check status or do other work
agent("Start my_arm waving using GR00T on port 5555, then check status")

# Stop
agent("Stop my_arm immediately")

Constructor parameters:

Parameter	Type	Description
tool_name	str	Name the agent uses to reference this robot
robot	str, RobotConfig, or Robot	Robot type string (e.g. "so101_follower"), a config object, or a pre-built robot instance
cameras	dict	Camera configuration mapping names to settings
port	str	Serial port for the robot (e.g. "/dev/ttyACM0")
data_config	str	Policy data configuration name
control_frequency	float	Control loop frequency in Hz (default: 50)
action_horizon	int	Number of actions to execute per inference step (default: 8)

Policy abstraction

Policies are the bridge between observations and actions. The library defines a Policy abstract class that any VLA model can implement:

from strands_robots import Policy, create_policy

# GR00T policy (ships with the library)
policy = create_policy(
    provider="groot",
    data_config="so100_dualcam",
    host="localhost",
    port=5555,
)

# Mock policy (for testing without hardware)
policy = create_policy(provider="mock")

The create_policy factory ships with "groot" and "mock" providers. You can integrate additional VLA models by subclassing Policy and implementing get_actions() and set_robot_state_keys().

Inference management

The gr00t_inference tool manages policy inference services running in Docker containers.

# Start with TensorRT acceleration
agent.tool.gr00t_inference(
    action="start",
    checkpoint_path="/data/checkpoints/model",
    port=5555,
    data_config="so100_dualcam",
    use_tensorrt=True,
)

# Check status
agent.tool.gr00t_inference(action="status", port=5555)

# Stop
agent.tool.gr00t_inference(action="stop", port=5555)

Available actions: start, stop, status, list, restart, and find_containers.

Additional tools

Beyond the core robot and inference tools, the library includes several utilities that the agent can use for setup, calibration, and data collection.

Camera tool

Camera management supporting OpenCV and RealSense cameras.

from strands_robots import lerobot_camera

agent = Agent(tools=[lerobot_camera])

agent("Discover all connected cameras")
agent("Capture images from front and wrist cameras")
agent("Record 30 seconds of video from the front camera")

Actions: discover, capture, capture_batch, record, preview, test.

Teleoperation tool

Record demonstrations for imitation learning using a leader-follower setup.

from strands_robots import lerobot_teleoperate

agent.tool.lerobot_teleoperate(
    action="start",
    robot_type="so101_follower",
    robot_port="/dev/ttyACM0",
    teleop_type="so101_leader",
    teleop_port="/dev/ttyACM1",
    dataset_repo_id="my_user/cube_picking",
    dataset_single_task="Pick up the red cube",
    dataset_num_episodes=50,
)

Actions: start, stop, list, replay.

Pose tool

Store, retrieve, and execute named robot poses for repeatable positioning.

from strands_robots import pose_tool

agent = Agent(tools=[robot, pose_tool])

agent("Save the current position as 'home'")
agent("Go to the home pose")
agent("Move the gripper to 50%")

Actions: store_pose, load_pose, list_poses, move_motor, incremental_move, reset_to_home.

Serial tool

Low-level serial communication for servos and custom protocols.

Actions: list_ports, feetech_position, feetech_ping, send, monitor.

Complete example

from strands import Agent
from strands_robots import Robot, gr00t_inference, lerobot_camera, pose_tool

robot = Robot(
    tool_name="orange_arm",
    robot="so101_follower",
    cameras={
        "wrist": {"type": "opencv", "index_or_path": "/dev/video0", "fps": 15},
        "front": {"type": "opencv", "index_or_path": "/dev/video2", "fps": 15},
    },
    port="/dev/ttyACM0",
    data_config="so100_dualcam",
)

agent = Agent(tools=[robot, gr00t_inference, lerobot_camera, pose_tool])

agent.tool.gr00t_inference(
    action="start",
    checkpoint_path="/data/checkpoints/gr00t-wave/checkpoint-300000",
    port=5555,
    data_config="so100_dualcam",
)

while True:
    user_input = input("\n> ")
    if user_input.lower() in ["exit", "quit"]:
        break
    agent(user_input)

agent.tool.gr00t_inference(action="stop", port=5555)

This gives you an interactive loop where you can issue natural language commands to the robot, check camera feeds, save poses, and manage inference services - all through conversation with the agent.

Links

• GitHub repository
• PyPI package
• NVIDIA Isaac GR00T
• LeRobot
• Jetson Containers