Robot Learning with Super-Linear Scaling

• URL: http://arxiv.org/abs/2412.01770v2
• Date: Fri, 06 Dec 2024 05:23:30 GMT
• Title: Robot Learning with Super-Linear Scaling
• Authors: Marcel Torne, Arhan Jain, Jiayi Yuan, Vidaaranya Macha, Lars Ankile, Anthony Simeonov, Pulkit Agrawal, Abhishek Gupta,
• Abstract summary: CASHER is a pipeline for scaling up data collection and learning in simulation where the performance scales superlinearly with human effort. We show that CASHER enables fine-tuning of pre-trained policies to a target scenario using a video scan without any additional human effort.
• Score: 20.730206708381704
• License:
• 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/

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