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Generalized Robot Learning Framework

Jiahuan Yan, Zhouyang Hong, Yu Zhao, Yu Tian, Yunxin Liu, Travis Davies, Luhui Hu

TL;DR

The paper presents a cost-efficient, end-to-end imitation learning framework for real-world robotics that runs on industrial-grade arms using common hardware. It demonstrates multi-task generalization across 10 real tasks with ~4,000 episodes, supported by a DDPM-based policy split into perception and action modules, and an objective Voting Positive Rate evaluation. The work emphasizes hardware-agnostic design, data efficiency (demonstrations rather than sheer model size), and open-source datasets/checkpoints to accelerate community progress. It also explores architecture ablations, data quality, and environmental generalization, offering practical guidance on task design, checkpoint selection, and deployment. The study highlights the potential for broader accessibility and collaborative advancement in embodied intelligence through low-cost hardware, simple data pipelines, and robust evaluation strategies.

Abstract

Imitation based robot learning has recently gained significant attention in the robotics field due to its theoretical potential for transferability and generalizability. However, it remains notoriously costly, both in terms of hardware and data collection, and deploying it in real-world environments demands meticulous setup of robots and precise experimental conditions. In this paper, we present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots, not just expensive collaborative robotic arms. Furthermore, our results show that multi-task robot learning is achievable with simple network architectures and fewer demonstrations than previously thought necessary. As the current evaluating method is almost subjective when it comes to real-world manipulation tasks, we propose Voting Positive Rate (VPR) - a novel evaluation strategy that provides a more objective assessment of performance. We conduct an extensive comparison of success rates across various self-designed tasks to validate our approach. To foster collaboration and support the robot learning community, we have open-sourced all relevant datasets and model checkpoints, available at huggingface.co/ZhiChengAI.

Generalized Robot Learning Framework

TL;DR

The paper presents a cost-efficient, end-to-end imitation learning framework for real-world robotics that runs on industrial-grade arms using common hardware. It demonstrates multi-task generalization across 10 real tasks with ~4,000 episodes, supported by a DDPM-based policy split into perception and action modules, and an objective Voting Positive Rate evaluation. The work emphasizes hardware-agnostic design, data efficiency (demonstrations rather than sheer model size), and open-source datasets/checkpoints to accelerate community progress. It also explores architecture ablations, data quality, and environmental generalization, offering practical guidance on task design, checkpoint selection, and deployment. The study highlights the potential for broader accessibility and collaborative advancement in embodied intelligence through low-cost hardware, simple data pipelines, and robust evaluation strategies.

Abstract

Imitation based robot learning has recently gained significant attention in the robotics field due to its theoretical potential for transferability and generalizability. However, it remains notoriously costly, both in terms of hardware and data collection, and deploying it in real-world environments demands meticulous setup of robots and precise experimental conditions. In this paper, we present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots, not just expensive collaborative robotic arms. Furthermore, our results show that multi-task robot learning is achievable with simple network architectures and fewer demonstrations than previously thought necessary. As the current evaluating method is almost subjective when it comes to real-world manipulation tasks, we propose Voting Positive Rate (VPR) - a novel evaluation strategy that provides a more objective assessment of performance. We conduct an extensive comparison of success rates across various self-designed tasks to validate our approach. To foster collaboration and support the robot learning community, we have open-sourced all relevant datasets and model checkpoints, available at huggingface.co/ZhiChengAI.
Paper Structure (22 sections, 13 figures, 1 table)

This paper contains 22 sections, 13 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Imitation Learning
  4. End-to-End Robot Learning
  5. Framework Setup
  6. Hardware Preparation
  7. Data Collection
  8. Robot Policy System
  9. Perception Module
  10. Action Prediction Module
  11. Task Design
  12. Voting Positive Rate
  13. Checkpoint Selection
  14. Model Deployment
  15. Experiments and Results
  16. ...and 7 more sections

Figures (13)

  • Figure 1: Overview of the framework: A real-world robot learning setup can be constructed using everyday household items, a robotic arm, a controller and two cameras.
  • Figure 2: End-to-End Framework: The pipeline illustrates the end-to-end process for a cost-efficient imitation learning implementation, from hardware setup and task design to data collection, modeling and training, evaluation (Voting Positive Rate), and model deployment. This framework is designed to be structurally simple and economically feasible for deployment.
  • Figure : (a) PickPlace
  • Figure : (a) PickPlace
  • Figure : (b) BlockPick
  • ...and 8 more figures