Human2Robot: Learning Robot Actions from Paired Human-Robot Videos

TL;DR

This paper introduces Human2Robot, a novel framework that learns fine-grained robot actions from paired human-robot videos, enabling better manipulation and generalization through a new dataset and a video prediction approach.

Contribution

It presents a new dataset H&R and a framework that leverages video prediction to improve robot learning from human demonstrations, especially for complex and novel tasks.

Findings

High performance on seen tasks

Significant one-shot generalization to new scenarios

Effective learning of fine-grained robot dynamics

Abstract

Distilling knowledge from human demonstrations is a promising way for robots to learn and act. Existing methods, which often rely on coarsely-aligned video pairs, are typically constrained to learning global or task-level features. As a result, they tend to neglect the fine-grained frame-level dynamics required for complex manipulation and generalization to novel tasks. We posit that this limitation stems from a vicious circle of inadequate datasets and the methods they inspire. To break this cycle, we propose a paradigm shift that treats fine-grained human-robot alignment as a conditional video generation problem. To this end, we first introduce H&R, a novel third-person dataset containing 2,600 episodes of precisely synchronized human and robot motions, collected using a VR teleoperation system. We then present Human2Robot, a framework designed to leverage this data. Human2Robot employs a Video Prediction Model to learn a rich and implicit representation of robot dynamics by generating robot videos from human input, which in turn guides a decoupled action decoder. Our real-world experiments demonstrate that this approach not only achieves high performance on seen tasks but also exhibits significant one-shot generalization to novel positions, objects, instances, and even new task categories.