Towards Human-level Intelligence via Human-like Whole-Body Manipulation

TL;DR

This paper introduces Astribot Suite, a comprehensive framework for developing general-purpose robots capable of human-like whole-body manipulation through imitation learning, addressing hardware, interface, and algorithmic challenges.

Contribution

The paper presents a unified system combining hardware, teleoperation interface, and learning algorithms to enable robots to perform diverse daily tasks with human-level dexterity.

Findings

Successful demonstration of diverse whole-body tasks

Effective learning of visuomotor policies from human demonstrations

Significant progress towards real-world general-purpose robots

Abstract

Building general-purpose intelligent robots has long been a fundamental goal of robotics. A promising approach is to mirror the evolutionary trajectory of humans: learning through continuous interaction with the environment, with early progress driven by the imitation of human behaviors. Achieving this goal presents three core challenges: (1) designing safe robotic hardware with human-level physical capabilities; (2) developing an intuitive and scalable whole-body teleoperation interface for data collection; and (3) creating algorithms capable of learning whole-body visuomotor policies from human demonstrations. To address these challenges in a unified framework, we propose Astribot Suite, a robot learning suite for whole-body manipulation aimed at general daily tasks across diverse environments. We demonstrate the effectiveness of our system on a wide range of activities that require whole-body coordination, extensive reachability, human-level dexterity, and agility. Our results show that Astribot's cohesive integration of embodiment, teleoperation interface, and learning pipeline marks a significant step towards real-world, general-purpose whole-body robotic manipulation, laying the groundwork for the next generation of intelligent robots.