Robot Learning with Super-Linear Scaling (2412.01770v2)

Abstract: Scaling robot learning requires data collection pipelines that scale favorably with human effort. In this work, we propose Crowdsourcing and Amortizing Human Effort for Real-to-Sim-to-Real(CASHER), a pipeline for scaling up data collection and learning in simulation where the performance scales superlinearly with human effort. The key idea is to crowdsource digital twins of real-world scenes using 3D reconstruction and collect large-scale data in simulation, rather than the real-world. Data collection in simulation is initially driven by RL, bootstrapped with human demonstrations. As the training of a generalist policy progresses across environments, its generalization capabilities can be used to replace human effort with model generated demonstrations. This results in a pipeline where behavioral data is collected in simulation with continually reducing human effort. We show that CASHER demonstrates zero-shot and few-shot scaling laws on three real-world tasks across diverse scenarios. We show that CASHER enables fine-tuning of pre-trained policies to a target scenario using a video scan without any additional human effort. See our project website: https://casher-robot-learning.github.io/CASHER/

• The paper demonstrates that robot learning performance scales super-linearly with sub-linear increases in human data collection.
• It employs a Real-to-Sim-to-Real pipeline that uses crowdsourced digital twin creation and simulation to efficiently train generalist policies.
• Experiments reveal robust zero-shot and few-shot improvements, achieving over 60% success rates across diverse real-world tasks.

Robot Learning with Super-Linear Scaling: An Academic Overview

The paper, "Robot Learning with Super-Linear Scaling," presents a novel approach to scaling data collection and training in robotic learning systems, emphasizing reducing human effort through leveraging simulation and crowdsourcing. The authors propose a systematic method named Crowdsourcing and Amortizing Human Effort for Real-to-Sim-to-Real, focusing on super-linear scaling performance with sub-linear human input.

Key Contributions and Methodology

The paper introduces a pipeline aimed at large-scale data acquisition and training of generalist policies. The pipeline, called Real-to-Sim-to-Real, effectively exploits digital twins for scalable robot learning. Its central innovations include:

1. Crowdsourced Digital Twin Creation: The paper highlights using 3D reconstruction technologies to crowdsource simulations of real-world environments. This twist shifts the data collection effort from expert designers to a broader non-expert audience and makes efficient use of mobile scanning applications.
2. Super-linear Data Scaling: The authors show that simulation-based learning can achieve super-linear increases in performance with less-than-linear increases in human effort, facilitated by deepening model generalization capabilities and reducing the role of direct human intervention over time.
3. Continual Learning and Amortization of Human Effort: A notable feature is the approach toward reducing human data collection needs by leveraging model generalization capabilities. The method iteratively learns across different environmental batches, where human demonstrations bootstrap initial training and are subsequently decreased as models generalize better.
4. Fine-tuning and Transferability: The research details methods for refining generalist policies with minimal human input, through either video scanned fine-tuning or few-shot demonstrations, enhancing performance in new real-world scenes without extensive retraining.

Experimental Framework

The authors conducted comprehensive experiments across varied tasks, notably involving object placement across different scene types, like placing dishes and opening cabinets. Key experimental results include:

• Demonstration of zero-shot and few-shot scaling laws across real-world tasks.
• Adoption of a multi-scene training approach resulting in significant increases in task performance even in untrained environments.
• Robustness checks addressing environmental variances, including multi-object scenarios and lighting changes.
• Validation and comparisons to highlight efficiency improvements in human effort versus performance scaling.

Numerical Results and Implications

A key numerical result is the ability to achieve substantial zero-shot performance improvements, as the number of training environments increases. For example, their pipeline achieves a significant success rate of over 60% as the number of trained environments scales up.

This approach's implications extend to both practical and theoretical realms. Practically, it provides a scalable framework for large-scale robotic applications without a proportional increase in human data collection workload. Theoretically, it opens inquiries into the nature of generalist policy training and its efficiency in simulating environments.

Future Directions

The paper indicates that while simulation presents inherent advantages, it shifts resource demands towards computational effort. Therefore, further developments could aim to balance this shift, potentially harnessing advancements in compute power and more efficient algorithms. A long-term vision might include the integration of more sophisticated autonomous data collection and learning mechanisms that minimize human supervision entirely.

In conclusion, this paper offers valuable insights and strategies within the robotics learning domain, particularly regarding scalable and efficient data collection methodologies in simulation. This robust framework of Crowdsourcing and Amortizing Human Effort for Real-to-Sim-to-Real could substantively impact the trajectory of robotic foundation model development and deployment.