RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot

TL;DR

This paper introduces RH20T, a large-scale, multi-modal robotic dataset with over 110,000 real-world manipulation sequences, designed to facilitate learning diverse, complex skills through one-shot imitation learning.

Contribution

The paper presents a comprehensive, high-quality dataset with multi-modal sensory data, human demonstrations, and language descriptions to advance generalizable robotic skill acquisition.

Findings

Dataset includes over 110,000 sequences across diverse skills and contexts.

Sequences contain visual, force, audio, and action data for rich perception.

High-quality calibration and real-world collection enhance dataset reliability.

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