mlsanityData to 13 Oct 2026

Autonomy 2.0: The Quest for Economies of Scale

arXiv 2307.03973 ↗First posted 8 Jul 2023cs.RO cs.AI cs.CYcited by 4

Not on the map. Why?

Abstract (arXiv)

With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines. In this new age of information technology, autonomous machines, such as service robots, autonomous drones, delivery robots, and autonomous vehicles, rather than humans, will provide services. In this article, through examining the technical challenges and economic impact of the digital economy, we argue that scalability is both highly necessary from a technical perspective and significantly advantageous from an economic perspective, thus is the key for the autonomy industry to achieve its full potential. Nonetheless, the current development paradigm, dubbed Autonomy 1.0, scales with the number of engineers, instead of with the amount of data or compute resources, hence preventing the autonomy industry to fully benefit from the economies of scale, especially the exponentially cheapening compute cost and the explosion of available data. We further analyze the key scalability blockers and explain how a new development paradigm, dubbed Autonomy 2.0, can address these problems to greatly boost the autonomy industry.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 19 of its 37 references that are in the map. First posted on arXiv 8 Jul 2023.

Builds on

19 of 37 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,106
  2. 2020Language Models are Few-Shot Learnerscited by 32,320
  3. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,889
  4. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,531
  5. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,215
  6. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 637
  7. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 569
  8. 2023Tri-Perspective View for Vision-Based 3D Semantic Occupancy Predictioncited by 406
  9. 2023Efficient Online Reinforcement Learning with Offline Datacited by 334
  10. 2023SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Drivingcited by 312
  11. 2021HDMapNet: An Online HD Map Construction and Evaluation Frameworkcited by 281
  12. 2023Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuningcited by 230
7 more
  1. 2023OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perceptioncited by 219
  2. 2022TransDreamer: Reinforcement Learning with Transformer World Modelscited by 143
  3. 2022Learning from All Vehiclescited by 135
  4. 2022MetaMorph: Learning Universal Controllers with Transformerscited by 79
  5. 2022How to Leverage Unlabeled Data in Offline Reinforcement Learningcited by 61
  6. 2023Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulatorscited by 21
  7. 2021Rise of the Autonomous Machinescited by 3

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.