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<title>Robot Learning on the Job</title> <script src="./src/popup.js" type="text/javascript"></script> <script type="text/javascript"> // redefining default features var _POPUP_FEATURES = 'width=500,height=300,resizable=1,scrollbars=1,titlebar=1,status=1'; </script> <style type="text/css" media="all"> body { font-family: "Merriweather","PT Serif",Georgia,"Times New Roman",serif; font-weight: 300; font-size:18px; margin-left: auto; margin-right: auto; width: 100%; color: #333332; }

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Robot Learning on the Job: Human-in-the-Loop Manipulation and Learning During Deployment

   Huihan Liu   Soroush Nasiriany   Lance Zhang   Zhiyao Bao   Yuke Zhu  

The University of Texas at Austin   

in submission to ICRA 2023    

Paper | Code

With the rapid growth of computing powers and recent advances in deep learning, we have witnessed impressive demonstrations of novel robot capabilities in research settings. Nonetheless, these learning systems exhibit brittle generalization and require excessive training data for practical tasks. To harness the capabilities of state-of-the-art robot learning models, while embracing their imperfections, we develop a principled framework for humans and robots to collaborate through a division of work. In this framework, partially autonomous robots are tasked with handling a major portion of decision-making where they work reliably; meanwhile, human operators monitor the process and intervene in challenging situations. Such a human-robot team ensures safe deployments in complex tasks. Further, we introduce a new learning algorithm to improve the policy's performance on the data collected from the task executions. The core idea is re-weighing training samples with approximated human trust and optimizing the policies with weighted behavioral cloning. We evaluate our framework in simulation and on real hardware, showing that our method consistently outperforms baselines over a collection of contact-rich manipulation tasks, achieving 8% boost in simulation and 27% on real hardware than the state-of-the-art methods, with 3 times faster convergence and 15% memory size.

Learning on the Job Overview

Continuous Deployment and Update Cycle

Method: Intervention-based Reweighting Scheme

Experiment Results

Our system ensures safe and reliable execution through human-robot teaming. We evaluated the autonomous policy performance of our human-in-the-loop framework on 4 tasks. As the autonomous policy improves over long-term deployment, the amount of human workload decreases.

We conduct 3 rounds of robot deployments and policy updates. Here we present Round 1 and Round 3 results of Ours and baseline IWR. We show how for Ours policy performance improve over rounds, and how Ours outperforms IWR baseline.

Gear Insertion (Real)

Ours, Round 1

IWR, Round 1

Ours, Round 3

IWR, Round 3

Coffee Pod packing (Real)

Ours, Round 1

IWR, Round 1

Ours, Round 3

IWR, Round 3

 

@inproceedings{jiang2022ditto,
   title={Ditto: Building Digital Twins of Articulated Objects from Interaction},
   author={Jiang, Zhenyu and Hsu, Cheng-Chun and Zhu, Yuke},
   booktitle={arXiv preprint arXiv:2202.08227},
   year={2022}
}
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