What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

2021/08/06 by Ajay Mandlekar, Mandlekar, Ajay, Danfei Xu +17 · 209 citations
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotics (cs.RO) #cs.AI #cs.LG #cs.RO

paper · pdf · doi:10.48550/arxiv.2108.03298

CoRL 2021 (Oral)

openalex publication_date 2021/08/06 · openalex created_date 2021/08/16 · arxiv created 2021/09/25 · arxiv updated 2021/09/28 · openalex updated_date 2026/07/28

Abstract

Imitating human demonstrations is a promising approach to endow robots with various manipulation capabilities. While recent advances have been made in imitation learning and batch (offline) reinforcement learning, a lack of open-source human datasets and reproducible learning methods make assessing the state of the field difficult. In this paper, we conduct an extensive study of six offline learning algorithms for robot manipulation on five simulated and three real-world multi-stage manipulation tasks of varying complexity, and with datasets of varying quality. Our study analyzes the most critical challenges when learning from offline human data for manipulation. Based on the study, we derive a series of lessons including the sensitivity to different algorithmic design choices, the dependence on the quality of the demonstrations, and the variability based on the stopping criteria due to the different objectives in training and evaluation. We also highlight opportunities for learning from human datasets, such as the ability to learn proficient policies on challenging, multi-stage tasks beyond the scope of current reinforcement learning methods, and the ability to easily scale to natural, real-world manipulation scenarios where only raw sensory signals are available. We have open-sourced our datasets and all algorithm implementations to facilitate future research and fair comparisons in learning from human demonstration data. Codebase, datasets, trained models, and more available at https://arise-initiative.github.io/robomimic-web/

Citations

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• Critic Regularized Regression
• Visual Imitation Made Easy
• Multi-Stage Learning for Grasp-Constrained Object Manipulation with a Simulated Panda Robot
• CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and\n Transfer Learning
• Human-in-the-Loop Imitation Learning using Remote Teleoperation
• COMBO: Conservative Offline Model-Based Policy Optimization
• Benchmarks for Deep Off-Policy Evaluation
• Decision Transformer: Reinforcement Learning via Sequence Modeling
• Transporter Networks: Rearranging the Visual World for Robotic Manipulation

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