Rerun's ECS Data Model Enables Physical AI Breakthroughs - Episode Hero Image

Rerun's ECS Data Model Enables Physical AI Breakthroughs

Gradient Dissent: Conversations on AI · · Listen to Original Episode →
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TL;DR

  • Adopting an Entity Component System (ECS) data model, inspired by game development, enables Rerun to handle complex, multimodal, and time-aware sensor data, making previously impossible AI tasks like flexible manipulation feel routine.
  • The core challenge in physical AI is not just algorithmic breakthroughs but also a robust data stack, where Rerun's ECS approach addresses the need for flexible, high-performance logging and visualization of time-varying sensor data.
  • Advanced manipulation in robotics, once elusive, is now achievable through end-to-end learning methods combining imitation and reinforcement learning, transitioning tasks like folding laundry from impossible to mundane.
  • The LLM revolution has primed the world for scalable learning in robotics, driving investment in hardware, data collection, and algorithmic innovations like transformers to overcome previous generalization issues.
  • Debugging complex multimodal systems in robotics and AI requires radically simpler visualization tools, enabling users to explore more data and uncover long-standing bugs in pipelines, as demonstrated by finding three-year-old issues.
  • Rerun's open-source logging SDK and visualization client prioritize low-friction adoption and embeddability, allowing users to customize and integrate visualization into their own applications without server dependencies.
  • The physical world's inherent long tail of variations presents a significant productization challenge for robots, requiring more than just task capability to include servicing, onboarding, and robust system integration.

Deep Dive

The core argument is that deploying AI in the physical world, particularly in robotics, is fundamentally bottlenecked by inadequate data tooling, necessitating a new approach to data modeling and management. This shift is critical because the unstructured, multimodal, and time-aware nature of physical world data demands a more flexible and performant system than traditional data stacks can provide, ultimately unlocking progress in complex tasks like manipulation.

The most significant second-order implication is the enabling of "boring" breakthroughs in robotics. By providing a robust data stack, Rerun allows developers to tackle previously intractable problems like flexible manipulation and advanced robotics with greater ease and speed. This means that tasks once considered science fiction, such as folding laundry, are becoming achievable through end-to-end learning methods, driven by the combination of imitation and reinforcement learning. Furthermore, the need for specialized data handling for physical AI highlights a divergence from traditional ML data pipelines, which are well-supported by existing tools like Parquet, Spark, and DataBricks for text-based LLMs. The unique characteristics of robotics data--multimodal, multi-rate, and episodic--require new file formats and indexing systems, as traditional tabular structures are insufficient. This necessity drives Rerun's custom-built, Rust-based solution, which leverages formats akin to sparse Parquet for efficient storage and querying of this complex data.

Another critical implication lies in the evolution of development workflows. The Rerun platform, inspired by Entity Component Systems (ECS) common in game development, offers a flexible data model that allows for "dump and forget" logging and dynamic querying. This contrasts with more rigid object-oriented approaches, enabling researchers to easily log and inspect novel data types without upfront schema definition. This flexibility is crucial for debugging complex multimodal systems that change over time, a challenge amplified in robotics. The platform's open-source logging SDK and visualization tools democratize access and embeddability, fostering broader adoption and integration within the robotics ecosystem. This approach is vital because traditional robotics middleware like ROS, while possessing strong network effects and an established ecosystem, is often criticized for its limitations and lack of comprehensive funding, suggesting a persistent need for more agile and specialized tooling.

The path to widespread adoption of advanced robotics also involves navigating the gap between impressive demos and deployable products. While cutting-edge research showcases remarkable manipulation capabilities, many practical applications are found in less publicized, scrappier deployments within manufacturing and logistics. These companies leverage similar AI models but focus on robustness and iterative deployment, often integrating open-source vision-language-action models. The challenge of building a complete product, encompassing not just the core AI but also servicing, onboarding, and managing the physical system, remains a significant hurdle for consumer-facing applications, suggesting that widespread autonomous home robots are still some time away, with simpler tasks like advanced vacuuming likely to lead the way.

Action Items

  • Audit Rerun's data model: Redesign for improved performance and storage efficiency, focusing on multi-rate and episodic data representation.
  • Implement Entity Component System (ECS) data model: Adapt for logging multimodal, time-aware sensor data to simplify debugging complex systems.
  • Create runbook template: Define 5 required sections (setup, common failures, rollback, monitoring) to prevent knowledge silos for physical AI applications.
  • Develop benchmarks for advanced manipulation tasks: Address the lack of standardized evaluation for AI-first robotics.
  • Integrate Rerun's logging SDK with ROS: Ensure seamless data streaming and visualization for robotics development workflows.

Related Episodes

Key Quotes

"we had this idea that visualization would be hard to monetize particularly for this like physical ai kind of applications you need to visualize every single step of everywhere you have might interact with it so we've actually redesigned the data model probably four times at this point"

Nikolaus West explains that visualization was initially seen as a difficult area for monetization, especially in physical AI applications. This challenge led Rerun to iterate significantly on their data model, redesigning it multiple times to better suit the complex needs of visualizing every step in physical AI interactions.

"so we're just really seeing an incredible progress in sort of ability to do quite advanced manipulation this has been a really difficult problem in robotics for a long time and another one is reinforcement learning and classically that's been for walking and motion and what's starting to work a lot now is kind of combining some of that reinforcement learning with imitation learning"

Nikolaus West highlights significant advancements in robotics, particularly in advanced manipulation tasks that have historically been challenging. He notes the progress in reinforcement learning, traditionally used for motion, and its increasing combination with imitation learning, which is now yielding strong results.

"so maybe we should start with like what are the range of use cases that you're typically used for and and how does rerun work there got it yeah so maybe just set the scene so rerun and we have an open source product and then there's a very popular uh and that project is an sdk for like logging modeling querying and visualizing like really multimodal data and particularly multimodal data that changes over time"

Nikolaus West describes Rerun's core offering as an SDK for logging, modeling, querying, and visualizing multimodal data that evolves over time. He indicates that this SDK serves a range of use cases, setting the stage for a discussion on how Rerun functions within those applications.

"so we designed a sort of new data model from scratch inspired by entity component systems this is like a way of modeling data in games so we couple of the early employees has gaming background and yeah basically we designed that entity component system"

Nikolaus West explains that Rerun developed a new data model inspired by Entity Component Systems (ECS), a concept commonly used in game development. This approach was chosen to provide flexibility in how users can log and visualize diverse data types, leveraging the gaming background of some team members.

"so we're just really seeing an incredible progress in in sort of the ability to do quite advanced manipulation so this has been a really difficult problem in robotics for a long time it's uh classic robotics is just like built around being incredibly precise you pre program every movement up front and just everything has to be done to the the sort of millimeter precise and but this kind of flexible messy manipulation and like folding laundry is a classic task has been really elusive for robotics for a long time and it's just sort of folding folding laundry has kind of gone from being impossible to sort of boring over the last year basically"

Nikolaus West elaborates on the progress in advanced manipulation for robotics, noting that tasks like folding laundry, once considered impossible, are now becoming "boring" due to recent advancements. He contrasts this with traditional robotics, which relies on pre-programmed, millimeter-precise movements.

"i think it's all of the above um my view tends to be that generally the things that matter more maybe more this like ecosystem or like economic engine or something like that i think what what i maybe see as the big arc is that the the llm hype chat gpt moment kind of thing really showed to the world the power of like really scalable machine learning and then that really primed everybody to be looking out for signs of could we have a scalable learning work for robotics"

Nikolaus West attributes the recent progress in robotics to a combination of factors, including algorithmic insights, training data, and hardware. He emphasizes that the broader impact of LLMs and scalable machine learning has primed the field to explore similar scalable learning approaches for robotics.

Resources

External Resources

Books

  • "The Hitchhiker's Guide to the Galaxy" by Douglas Adams - Mentioned as an example of a book with a unique data model.

Articles & Papers

  • "Gradient Descent" (Podcast) - Mentioned as the name of the podcast series.

People

  • Nikolaus West - CEO of Rerun, guest on the podcast.
  • Lukas Biewald - Host of Gradient Descent, CEO of Weights & Biases.
  • Voitcheck - Co-founder of OpenAI, mentioned in relation to early robotics projects.

Organizations & Institutions

  • Rerun - Company developing logging and data platform for robotics.
  • Weights & Biases - Company providing tools for AI teams, similar domain to Rerun.
  • OpenAI - Mentioned in relation to early robotics projects.
  • NFL (National Football League) - Mentioned in relation to sports discussion.
  • New England Patriots - Mentioned as an example team for performance analysis.
  • Pro Football Focus (PFF) - Data source for player grading.
  • Hugging Face - Mentioned for its role in releasing open-source models for robotics.
  • Boston Dynamics - Mentioned as an example of traditional robotics.
  • Matic - Mentioned as a robot vacuum vendor.
  • Roomba - Mentioned as a robot vacuum vendor.
  • Generalist AI - Mentioned as an underrated robotics startup.
  • Ultra - Mentioned as a robotics company focused on pick and place for logistics.
  • C-React - Mentioned as a European company focused on manufacturing robotics.
  • Nvidia - Mentioned for its new simulator engine.

Tools & Software

  • Entity Component System (ECS) - Data model inspired by games, used by Rerun.
  • ROS (Robot Operating System) - Widely used system in robotics, known for its ecosystem and message-passing capabilities.
  • Spark - High-scale data processing engine.
  • Databricks - Mentioned in relation to data processing tools.
  • Streamlit - Tool for creating custom visualizations.
  • Murimo - Tool for creating custom visualizations.
  • Codex - Mentioned in relation to agents building specialized tools.
  • Cloud - Mentioned in relation to agents building specialized tools.

Websites & Online Resources

  • Rerun (rerun.io) - Website for the company Rerun.
  • Weights & Biases (wandb) - Mentioned in relation to company LinkedIn.
  • Nikolaus West (linkedin.com/in/nikolauswest/) - LinkedIn profile.
  • Rerun (linkedin.com/company/rerun-io/) - Company LinkedIn profile.
  • Lukas Biewald (linkedin.com/in/lbiewald/) - LinkedIn profile.

Other Resources

  • Physical AI - Concept discussed as the future of AI.
  • Entity Component System (ECS) - Data model used by Rerun.
  • Imitation Learning - Machine learning paradigm used in robotics.
  • Reinforcement Learning - Machine learning paradigm used in robotics.
  • Transformers - Mentioned as an innovation in robotics modeling.
  • LLM (Large Language Model) - Mentioned in relation to AI advancements.
  • GPT - Mentioned in relation to OpenAI's past focus.
  • Vision Language Action (VLA) Models - Mentioned in relation to robotics applications.
  • Teleoperation - Mentioned in relation to robotics control.
  • Parquet - Data file format.
  • Arrow - Data format used by Rerun.
  • Physical Data - Data characterized as multimodal, multi-rate, and episodic.
  • Simulation - Mentioned as a method for training AI systems.
  • UMI - Mentioned as a project by Generalist AI.
  • Robot Fighting League - Mentioned as an event.
  • Humanoid Robots - Mentioned as a consumer product.
  • Robot Dogs - Mentioned as a type of AI-powered toy.
  • Agricultural Picking - Application of robotics.
  • Last-Mile Delivery - Application of robotics.
  • Drones - Mentioned as an application area.
  • AR (Augmented Reality) - Application area for Rerun.
  • Spatial Computing - Application area for Rerun.
  • Embodied AI - Broader category of AI applications.
  • Film Production - Application area for AI.
  • Security Surveillance - Application area for AI.
  • Construction Analytics - Application area for AI.
  • Satellite Data - Mentioned as a potential application area.
  • Force and Tactile Touch - Emerging sensor types for robotics.
  • Audio - Mentioned as an emerging data type for robotics.
  • Benchmarks - Discussed in the context of robotics evaluation.
  • Message Passing System - Core concept of ROS.
  • Message Definitions - Standardized data formats in ROS.
  • Navigation Module - Example of a pluggable system in ROS.
  • General Robotics - Market segment with high valuations.
  • Humanoid Form Factor - Discussed as a potential for home tasks.
  • Semi-Humanoid Form Factor - Mentioned as a practical alternative.
  • Data Annotation - Application of LLMs in robotics.
  • Computing Embeddings - Used for search and curation.
  • Schema - Data structure definition.
  • Teleoperation - Mentioned in relation to low-latency requirements.
  • Pull Requests - Contribution mechanism for open-source projects.
  • Trust - Benefit of open-source projects.
  • Robot Project (Hugging Face) - Open-source project integrating Rerun.
  • Simulator Engine (Nvidia) - Simulator engine integrating Rerun.
  • SQL Query - Standard for database querying.

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