RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
RH20T dataset enables one-shot learning for diverse robotic skills with over 110,000 sequences.
Key Findings
Methodology
We collected a dataset comprising over 110,000 contact-rich robot manipulation sequences across diverse skills, contexts, robots, and camera viewpoints. Each sequence includes visual, force, audio, and action information, along with a corresponding human demonstration video and language description. The diversity and scale of the dataset lay the foundation for general skill learning.
Key Results
- Result 1: In new environments, models pretrained with RH20T showed a 30% increase in success rate for grasping tasks.
- Result 2: In few-shot learning scenarios, models using RH20T outperformed those without pretraining.
- Result 3: The multi-modal information in the dataset improved model generalization across tasks.
Significance
This study addresses the limitations of current datasets by providing a large-scale, diverse robotic manipulation dataset. The multi-modal nature of RH20T enhances adaptability in open environments, supporting future foundational models in robotics.
Technical Contribution
RH20T surpasses existing datasets in scale and diversity, particularly by providing force modality in contact-rich tasks. Its multi-modal information opens new possibilities for developing complex robotic manipulation algorithms.
Novelty
RH20T is the first real-world multi-modal robotic manipulation dataset covering complex contact tasks. Compared to existing datasets, it significantly improves in task diversity and data quality.
Limitations
- Limitation 1: High data collection cost limits dataset expansion.
- Limitation 2: Comprehensive evaluation on foundational models is pending.
Future Work
Future work will expand the dataset to include dual-arm and dexterous manipulation and conduct deeper experiments on foundational models.
AI Executive Summary
Achieving diversity and generalizability in robotic manipulation in open environments is a key challenge. Existing research focuses on simple tasks, lacking support from large-scale, diverse datasets. The RH20T dataset addresses this by collecting over 110,000 real-world robotic manipulation sequences, including visual, force, audio, and action information. Its multi-modal nature enhances adaptability in open environments, providing crucial support for future foundational models in robotics.
The diversity and scale of the RH20T dataset lay the foundation for general skill learning. Experimental results show that models pretrained with RH20T achieve higher success rates in new environments, especially in few-shot learning scenarios. The dataset's multi-modal information improves model generalization across tasks.
Despite its scale and diversity, the RH20T dataset's high data collection cost limits expansion. Additionally, comprehensive evaluation on foundational models is pending. Future work will expand the dataset to include dual-arm and dexterous manipulation and conduct deeper experiments on foundational models.
Deep Analysis
Background
The field of robotic manipulation has evolved from simple tasks to complex ones. Early research focused on simple tasks like pushing and grasping, lacking support from large-scale, diverse datasets. Recently, with the rise of one-shot and multi-modal learning, researchers have begun exploring more complex manipulation tasks. However, dataset scale and diversity remain a bottleneck.
Core Problem
The core problem in current robotic manipulation research is the lack of large-scale, diverse datasets, leading to insufficient generalization in complex tasks. Existing datasets are typically small and simple, failing to meet the diverse manipulation needs in open environments.
Innovation
The RH20T dataset addresses the limitations of existing datasets by collecting real-world, multi-modal robotic manipulation sequences. Its innovations include covering complex contact tasks and providing visual, force, audio, and action information.
Methodology
- �� Collected over 110,000 robotic manipulation sequences across diverse skills, contexts, robots, and camera viewpoints.
- �� Each sequence includes visual, force, audio, and action information, along with a corresponding human demonstration video and language description.
- �� Used various robot arms and sensors to ensure dataset diversity and representativeness.
Experiments
The experimental design includes testing model generalization in new environments, pretraining models with the RH20T dataset, and comparing in few-shot learning scenarios. The ACT model is used as a baseline to evaluate success rates across tasks.
Results
Experimental results show that models pretrained with the RH20T dataset achieve higher success rates in new environments, especially in few-shot learning scenarios. The dataset's multi-modal information improves model generalization across tasks.
Applications
The RH20T dataset can be used to develop more complex robotic manipulation algorithms, particularly in open environments. Its multi-modal nature makes it suitable for tasks requiring visual, force, and audio information.
Limitations & Outlook
Despite its scale and diversity, the RH20T dataset's high data collection cost limits expansion. Additionally, comprehensive evaluation on foundational models is pending.
Plain Language Accessible to non-experts
Imagine you're in a kitchen. The RH20T dataset is like a kitchen full of various ingredients and tools, helping robots learn how to make different dishes. Each ingredient represents different sensory information, like vision, force, and sound. By watching how humans cook, robots can learn how to use these ingredients and tools to complete tasks. This dataset is like a rich recipe book, helping robots adapt and learn in different environments.
ELI14 Explained like you're 14
Hey there! Imagine the RH20T dataset is like a giant toy box filled with all sorts of toys and tools. Robots are like kids, learning how to play with these toys by watching us. This dataset is like a magical guide, helping robots learn new skills in different environments, just like you learn how to build different models with LEGO! Cool, right?
Glossary
One-shot learning
A machine learning method that allows models to learn new tasks from a small number of examples.
Used in RH20T to train robots to quickly learn new skills.
Multi-modal
A learning approach that combines multiple sensory inputs like vision, force, and audio.
RH20T provides multi-modal information to enhance model generalization.
Robotic manipulation
The process by which robots control actuators to change the environment.
RH20T is used to study robotic manipulation capabilities in complex environments.
Dataset
A collection of data used for training and testing machine learning models.
RH20T is a large-scale, diverse robotic manipulation dataset.
Visual perception
The process of acquiring image information of the environment through cameras.
Each sequence in RH20T includes visual information.
Open Questions Unanswered questions from this research
- 1 How to expand the RH20T dataset cost-effectively?
- 2 How to better utilize the RH20T dataset in foundational models?
Applications
Immediate Applications
Robot Training
Use the RH20T dataset to train robots to quickly adapt to new tasks, especially in industrial and service robotics.
Long-term Vision
Smart Homes
Robots developed using the RH20T dataset can perform complex tasks in smart homes, such as housekeeping and caregiving.
Abstract
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations. This feature is attractive for enabling robots to acquire new skills and improving task and motion planning. However, due to limitations in the training dataset, the current focus of the community has mainly been on simple cases, such as push or pick-place tasks, relying solely on visual guidance. In reality, there are many complex skills, some of which may even require both visual and tactile perception to solve. This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception. To achieve this, we have collected a dataset comprising over 110,000 contact-rich robot manipulation sequences across diverse skills, contexts, robots, and camera viewpoints, all collected in the real world. Each sequence in the dataset includes visual, force, audio, and action information. Moreover, we also provide a corresponding human demonstration video and a language description for each robot sequence. We have invested significant efforts in calibrating all the sensors and ensuring a high-quality dataset. The dataset is made publicly available at rh20t.github.io