Remembr

<h1 align="center">ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robots</h1> <p align="center"><a href="https://arxiv.org/abs/2409.13682">Paper</a> - <a href="https://nvidia-ai-iot.github.io/remembr">Website</a><br><a href="#setup">Setup</a> -<a href="#usage">Usage</a> - <a href="#examples">Examples</a> - <a href="#evaluation">Evaluation</a> - <a href="#notice">Notice</a> - <a href="#see_also">See also</a> </p>

ReMEmbR is a project that uses LLMs + VLMs to build and reason over long horizon spatio-temporal memories.

This allows robots to reason over many kinds of questions such as "Hey Robot, can you take take me to get snacks?" to temporal multi-step reasoning questions like "How long were you in the building for?"

<a id="setup"></a>

Setup

1. Download VILA

   mkdir deps
   cd deps
   git clone https://github.com/NVlabs/VILA.git
   ./vila_setup.sh remembr
   

2. Install OLLama

   curl -fsSL https://ollama.com/install.sh | sh
   

3. Install other Python dependencies

   conda activate remembr
   python -m pip install -r requirements.txt
   

4. Install MilvusDB

 curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh -o launch_milvus_container.sh

> `docker` must be installed on the system to easily use Milvus by simply running the command below. This script will automatically launch MilvusDB on a docker container. Otherwise, the user must install MilvusDB from scratch themselves

```
bash launch_milvus_container.sh start
```

<a id="usage"></a>

Usage

Step 1- Create a Memory database

Before you can use the ReMEmbR agent, you need to store data. The Memory class provides an standard interface for storing and retrieving data that can be used by the agent.

Here we use the pre-defined MilvusMemory class which implements this interface and uses MilvusDB under the hood.

from remembr.memory.memory import MemoryItem
from remembr.memory.milvus_memory import MilvusMemory

memory = MilvusMemory("test_collection", db_ip='127.0.0.1')

memory.reset()

Step 2 - Add a MemoryItem

The data used by ReMEmbR includes captions (as generated from a VLM) along with associated timestamps and pose information (from a SLAM algorithm or other source).

You can populate the memory database by inserting items as follows.

from remembr.memory.memory import MemoryItem

memory_item = MemoryItem(
    caption="I see a desk", 
    time=1.1, 
    position=[0.0, 0.0, 0.0], 
    theta=3.14
)

memory.insert(memory_item)

In practice, you will generate the MemoryItems from different sources, like a ROS2 bag, dataset, or from a real robot.

Step 3 - Create the ReMEmbR agent

Now that you've populated your memory database, let's create the ReMEmbR agent to reason over it.

The agent is LLM-agnostic, here we show ReMEmbR using the command-r LLM type. We point it to the memory we created above.

from remembr.agents.remembr_agent import ReMEmbRAgent

agent = ReMEmbRAgent(llm_type='command-r')

agent.set_memory(memory)

Step 3 - Run the agent!

That's it! Now we can ask the agent questions, and get back structured answers, including goal poses.

response = agent.query("Where can I sit?")

print(response.position)

# uncomment if you want to see the text reason for the position data
# print(response.text) 

<a id="examples"></a>

Examples

Example 1 - ROS Bag Gradio Demo (offline)

1. Follow the setup above

2. Run the demo

   cd examples/chat_demo
   python demo.py
   

3. Open your web browser to load your ROSBag and query the agent

Note RCL import error If you recieve an error such as version 'GLIBCXX_3.4.30' not found, you may need to update your gcc version conda install -c conda-forge gcc=12.1.0

Example 2 - Nova Carter Demo (live)

Please check the nova_carter_demo folder for details.

<a id="evaluation"></a>

Dataset and Evaluation

If you are interested in the NaVQA dataset and evaluating on it, please check the evaluation readme for more information.

<a id="notice"></a>

Usage Notice!

This project depends on the following third-party open source software projects. Review the license terms of these open source projects before use. These projects may download models or data, you should refer to the licenses in these projects for usage regarding those components.

These projects are

1. WhisperTRT: https://github.com/NVIDIA-AI-IOT/whisper_trt
2. MilvusDB: https://github.com/milvus-io/milvus
3. VILA: https://github.com/NVlabs/VILA
4. ROS2 rclpy: https://github.com/ros2/rclpy
5. Gradio: https://github.com/gradio-app/gradio
6. LangGraph: https://langchain-ai.github.io/langgraph/

Please refer to the LICENSE.md file for details regarding the usage of the code directly authored / contained in this repository.

<a id="see_also"></a>

See Also

• Isaac ROS Documentation - Our Nova Carter demo uses Isaac ROS for navigation. The documentation includes details for how to build maps, run end-to-end navigation. Check it out!
• Jetson AI Lab - Many examples for using LLMs, VLMs, and zero-shot models on NVIDIA Jetson.