embodied agents
1.0.0
Docs: docs
Simple Robot Agent Example:
Simulation Example with SimplerEnv:
? Motor Agent using OpenVLA:
⏺️ Record Dataset on a Robot
? Support, Discussion, and How-To's :
Updates:
Aug 28 2024, embodied-agents v1.2
mbodied for trying them.June 30 2024, embodied-agents v1.0:
mbodied.embodied agents is a toolkit for integrating large multi-modal models into existing robot stacks with just a few lines of code. It provides consistency, reliability, scalability and is configurable to any observation and action space.
The Sample class is a base model for serializing, recording, and manipulating arbitrary data. It is designed to be extendable, flexible, and strongly typed. By wrapping your observation or action objects in the Sample class, you'll be able to convert to and from the following with ease:
To learn more about all of the possibilities with embodied agents, check out the documentation
pack a list of Samples or Dicts into a single Sample or Dict and unpack accordingly?unflatten any python structure into a Sample class so long you provide it with a valid json schema?Creating a Sample requires just wrapping a python dictionary with the Sample class. Additionally, they can be made from kwargs, Gym Spaces, and Tensors to name a few.
from mbodied.types.sample import Sample
# Creating a Sample instance
sample = Sample(observation=[1,2,3], action=[4,5,6])
# Flattening the Sample instance
flat_list = sample.flatten()
print(flat_list) # Output: [1, 2, 3, 4, 5, 6]
# Generating a simplified JSON schema
>>> schema = sample.schema()
{'type': 'object', 'properties': {'observation': {'type': 'array', 'items': {'type': 'integer'}}, 'action': {'type': 'array', 'items': {'type': 'integer'}}}}
# Unflattening a list into a Sample instance
Sample.unflatten(flat_list, schema)
>>> Sample(observation=[1, 2, 3], action=[4, 5, 6])The Sample class leverages Pydantic's powerful features for serialization and deserialization, allowing you to easily convert between Sample instances and JSON.
# Serialize the Sample instance to JSON
sample = Sample(observation=[1,2,3], action=[4,5,6])
json_data = sample.model_dump_json()
print(json_data) # Output: '{"observation": [1, 2, 3], "action": [4, 5, 6]}'
# Deserialize the JSON data back into a Sample instance
json_data = '{"observation": [1, 2, 3], "action": [4, 5, 6]}'
sample = Sample.model_validate(from_json(json_data))
print(sample) # Output: Sample(observation=[1, 2, 3], action=[4, 5, 6])# Converting to a dictionary
sample_dict = sample.to("dict")
print(sample_dict) # Output: {'observation': [1, 2, 3], 'action': [4, 5, 6]}
# Converting to a NumPy array
sample_np = sample.to("np")
print(sample_np) # Output: array([1, 2, 3, 4, 5, 6])
# Converting to a PyTorch tensor
sample_pt = sample.to("pt")
print(sample_pt) # Output: tensor([1, 2, 3, 4, 5, 6])gym_space = sample.space()
print(gym_space)
# Output: Dict('action': Box(-inf, inf, (3,), float64), 'observation': Box(-inf, inf, (3,), float64))See sample.py for more details.
The Message class represents a single completion sample space. It can be text, image, a list of text/images, Sample, or other modality. The Message class is designed to handle various types of content and supports different roles such as user, assistant, or system.
You can create a Message in versatile ways. They can all be understood by mbodi's backend.
from mbodied.types.message import Message
Message(role="user", content="example text")
Message(role="user", content=["example text", Image("example.jpg"), Image("example2.jpg")])
Message(role="user", content=[Sample("Hello")])The Backend class is an abstract base class for Backend implementations. It provides the basic structure and methods required for interacting with different backend services, such as API calls for generating completions based on given messages. See backend directory on how various backends are implemented.
Agent is the base class for various agents listed below. It provides a template for creating agents that can talk to a remote backend/server and optionally record their actions and observations.
The Language Agent can connect to different backends or transformers of your choice. It includes methods for recording conversations, managing context, looking up messages, forgetting messages, storing context, and acting based on an instruction and an image.
Natively supports API services: OpenAI, Anthropic, vLLM, Ollama, HTTPX, or any gradio endpoints. More upcoming!
To use OpenAI for your robot backend:
from mbodied.agents.language import LanguageAgent
agent = LanguageAgent(context="You are a robot agent.", model_src="openai")To execute an instruction:
instruction = "pick up the fork"
response = robot_agent.act(instruction, image)Language Agent can connect to vLLM as well. For example, suppose you are running a vLLM server Mistral-7B on 1.2.3.4:1234. All you need to do is:
agent = LanguageAgent(
context=context,
model_src="openai",
model_kwargs={"api_key": "EMPTY", "base_url": "http://1.2.3.4:1234/v1"},
)
response = agent.act("Hello, how are you?", model="mistralai/Mistral-7B-Instruct-v0.3")Example using Ollama:
agent = LanguageAgent(
context="You are a robot agent.", model_src="ollama",
model_kwargs={"endpoint": "http://localhost:11434/api/chat"}
)
response = agent.act("Hello, how are you?", model="llama3.1")Motor Agent is similar to Language Agent but instead of returning a string, it always returns a Motion. Motor Agent is generally powered by robotic transformer models, i.e. OpenVLA, RT1, Octo, etc.
Some small model, like RT1, can run on edge devices. However, some, like OpenVLA, may be challenging to run without quantization. See OpenVLA Agent and an example OpenVLA server
These agents interact with the environment to collect sensor data. They always return a SensorReading, which can be various forms of processed sensory input such as images, depth data, or audio signals.
Currently, we have:
agents that process robot's sensor information.
Auto Agent dynamically selects and initializes the correct agent based on the task and model.
from mbodied.agents.auto.auto_agent import AutoAgent
# This makes it a LanguageAgent
agent = AutoAgent(task="language", model_src="openai")
response = agent.act("What is the capital of France?")
# This makes it a motor agent: OpenVlaAgent
auto_agent = AutoAgent(task="motion-openvla", model_src="https://api.mbodi.ai/community-models/")
action = auto_agent.act("move hand forward", Image(size=(224, 224)))
# This makes it a sensory agent: DepthEstimationAgent
auto_agent = AutoAgent(task="sense-depth-estimation", model_src="https://api.mbodi.ai/sense/")
depth = auto_agent.act(image=Image(size=(224, 224)))Alternatively, you can use get_agent method in auto_agent as well.
language_agent = get_agent(task="language", model_src="openai")The motion_controls module defines various motions to control a robot as Pydantic models. They are also subclassed from Sample, thus possessing all the capability of Sample as mentioned above. These controls cover a range of actions, from simple joint movements to complex poses and full robot control.
You can integrate your custom robot hardware by subclassing Robot quite easily. You only need to implement do() function to perform actions (and some additional methods if you want to record dataset on the robot). In our examples, we use a mock robot. We also have an XArm robot as an example.
Recording a dataset on a robot is very easy! All you need to do is implement the get_observation(), get_state(), and prepare_action() methods for your robot. After that, you can record a dataset on your robot anytime you want. See examples/5_teach_robot_record_dataset.py and this colab: for more details.
from mbodied.robots import SimRobot
from mbodied.types.motion.control import HandControl, Pose
robot = SimRobot()
robot.init_recorder(frequency_hz=5)
with robot.record("pick up the fork"):
motion = HandControl(pose=Pose(x=0.1, y=0.2, z=0.3, roll=0.1, pitch=0.2, yaw=0.3))
robot.do(motion)Dataset Recorder is a lower level recorder to record your conversation and the robot's actions to a dataset as you interact with/teach the robot. You can define any observation space and action space for the Recorder. See gymnasium for more details about spaces.
from mbodied.data.recording import Recorder
from mbodied.types.motion.control import HandControl
from mbodied.types.sense.vision import Image
from gymnasium import spaces
observation_space = spaces.Dict({
'image': Image(size=(224, 224)).space(),
'instruction': spaces.Text(1000)
})
action_space = HandControl().space()
recorder = Recorder('example_recorder', out_dir='saved_datasets', observation_space=observation_space, action_space=action_space)
# Every time robot makes a conversation or performs an action:
recorder.record(observation={'image': image, 'instruction': instruction,}, action=hand_control)The dataset is saved to ./saved_datasets.
The Replayer class is designed to process and manage data stored in HDF5 files generated by Recorder. It provides a variety of functionalities, including reading samples, generating statistics, extracting unique items, and converting datasets for use with HuggingFace. The Replayer also supports saving specific images during processing and offers a command-line interface for various operations.
Example for iterating through a dataset from Recorder with Replayer:
from mbodied.data.replaying import Replayer
replayer = Replayer(path=str("path/to/dataset.h5"))
for observation, action in replayer:
...├─ assets/ ............. Images, icons, and other static assets
├─ examples/ ........... Example scripts and usage demonstrations
├─ resources/ .......... Additional resources for examples
├─ src/
│ └─ mbodied/
│ ├─ agents/ ....... Modules for robot agents
│ │ ├─ backends/ .. Backend implementations for different services for agents
│ │ ├─ language/ .. Language based agents modules
│ │ ├─ motion/ .... Motion based agents modules
│ │ └─ sense/ ..... Sensory, e.g. audio, processing modules
│ ├─ data/ ......... Data handling and processing
│ ├─ hardware/ ..... Hardware modules, i.e. camera
│ ├─ robot/ ........ Robot interface and interaction
│ └─ types/ ........ Common types and definitions
└─ tests/ .............. Unit testsWe welcome issues, questions and PRs. See the contributing guide for more information.
