Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminaries 2 Preliminaries 3 TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction and 3.1 Learning Base Policies in Simulation with RL 3 TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction and 3.1 Learning Base Policies in Simulation with RL 3.2 Learning Residual Policies from Online Correction 3.2 Learning Residual Policies from Online Correction 3.3 An Integrated Deployment Framework and 3.4 Implementation Details 3.3 An Integrated Deployment Framework and 3.4 Implementation Details 4 Experiments 4 Experiments 4.1 Experiment Settings 4.1 Experiment Settings 4.2 Quantitative Comparison on Four Assembly Tasks 4.2 Quantitative Comparison on Four Assembly Tasks 4.3 Effectiveness in Addressing Different Sim-to-Real Gaps (Q4) 4.3 Effectiveness in Addressing Different Sim-to-Real Gaps (Q4) 4.4 Scalability with Human Effort (Q5) and 4.5 Intriguing Properties and Emergent Behaviors (Q6) 4.4 Scalability with Human Effort (Q5) and 4.5 Intriguing Properties and Emergent Behaviors (Q6) 5 Related Work 5 Related Work 6 Conclusion and Limitations, Acknowledgments, and References 6 Conclusion and Limitations, Acknowledgments, and References A. Simulation Training Details A. Simulation Training Details B. Real-World Learning Details B. Real-World Learning Details C. Experiment Settings and Evaluation Details C. Experiment Settings and Evaluation Details D. Additional Experiment Results D. Additional Experiment Results 2 Preliminaries 2.1 Problem Formulation 2.2 Intervention-Based Policy Learning 3 TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctio An overview of TRANSIC is shown in Fig. 2. At a high level, after training the base policy in simulation, we deploy it on the real robot while monitored by a human operator. The human interrupts the autonomous execution when necessary and provides online correction through teleoperation. Such intervention and online correction are collected to train a residual policy, after which both base and residual policies are deployed to complete contact-rich manipulation tasks. In this section, we first elaborate on the simulation training phase with several important design choices that reduce sim-to-real gaps before transfer. We then introduce residual policies learned from human intervention and online correction. Subsequently, we present an integrated framework for deploying the base policy alongside the learned residual policy during testing. Finally, we provide implementation details. 3.1 Learning Base Policies in Simulation with RL 3.2 Learning Residual Policies from Online Correctio 3.3 An Integrated Deployment Framework 3.4 Implementation Details We use Isaac Gym [10] as the simulation backend. Proximal policy optimization (PPO [84]) is used to train teacher policies from scratch. We design task-specific reward functions and curricula when necessary to facilitate RL training. We apply exhaustive domain randomization during teacher policy training and proper data augmentation during student policy distillation. Student policies are parameterized as Gaussian Mixture Models (GMMs [68]). We have also experimented with other state-of-the-art policy models, such as Diffusion Policy [85], but did not observe better performances. See the Appendix Sec. A for more details about the simulation training phase and additional comparisons. During the human-in-the-loop data collection phase, we use a 3Dconnexion SpaceMouse as the teleoperation interface. Residual policies use state-of-the-art point cloud encoders, such as PointNet [86] and Perceiver [87, 88], and GMM as the action head. We follow the best practices to train residual policies, including using learning rate warm-up and cosine annealing [89]. More training hyperparameters are provided in the Appendix Sec. B.4. Authors:
(1) Yunfan Jiang, Department of Computer Science;
(2) Chen Wang, Department of Computer Science;
(3) Ruohan Zhang, Department of Computer Science and Institute for Human-Centered AI (HAI);
(4) Jiajun Wu, Department of Computer Science and Institute for Human-Centered AI (HAI);
(5) Li Fei-Fei, Department of Computer Science and Institute for Human-Centered AI (HAI). Authors: Authors: (1) Yunfan Jiang, Department of Computer Science; (2) Chen Wang, Department of Computer Science; (3) Ruohan Zhang, Department of Computer Science and Institute for Human-Centered AI (HAI); (4) Jiajun Wu, Department of Computer Science and Institute for Human-Centered AI (HAI); (5) Li Fei-Fei, Department of Computer Science and Institute for Human-Centered AI (HAI). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

The TRANSIC Recipe: Mix Simulation, Add Human Touch, Deploy Successfully

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

AI Tutor Is Real, And It's Already Here

AI and Crowdsourcing: Using Human-in-the-Loop Labeling

Introduction to Human In The Loop Machine Learning

Roughly Half of Data Scientists Consider Model Monitoring a Major Nuisance: Does It Have to Be So?

Using Human-in-the-Loop Approach in Machine Learning

What is Human-in-the-Loop for Machine Learning?

AI Tutor Is Real, And It's Already Here

AI and Crowdsourcing: Using Human-in-the-Loop Labeling

Introduction to Human In The Loop Machine Learning

Roughly Half of Data Scientists Consider Model Monitoring a Major Nuisance: Does It Have to Be So?

Using Human-in-the-Loop Approach in Machine Learning

What is Human-in-the-Loop for Machine Learning?

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps