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 \emph{contact-rich} robot manipulation
sequences across diverse skills, contexts, robots, and camera viewpoints, all
collected \emph{in the real world}. Each sequence in the dataset includes
visual, force, audio, and action information, along with a corresponding human
demonstration video. 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

本研究旨在解锁机器人实现多模感知的能力，以通过单次模仿学习技术普及各种真实世界技能，通过收集千万级别的真实世界接触式机器人操作记录构建的开源数据集提高任务和动作规划的性能。