Controlling diverse robots by inferring Jacobian fields with deep networks

TL;DR

This paper presents a deep learning-based method to control diverse robots using only a single camera, without prior knowledge of their materials or sensing capabilities, enabling broad and accessible robotic automation.

Contribution

The authors introduce a novel approach that infers Jacobian fields from video streams to control various robots without expert intervention or detailed models.

Findings

Achieves accurate closed-loop control across diverse robot types.

Recovers the causal dynamic structure of each robot.

Operates using only a single camera as the sensor.

Abstract

Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics. Modern fabrication techniques have greatly expanded the feasible hardware, but using these systems requires control software to translate the desired motions into actuator commands. Conventional robots can easily be modeled as rigid links connected by joints, but it remains an open challenge to model and control biologically inspired robots that are often soft or made of several materials, lack sensing capabilities, and may change their material properties with use. Here, we introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field (the sensitivity of all 3D points to the robot's actuators). Our method enables the control of robots from only a single camera, makes no assumptions about the robots' materials, actuation, or sensing, and is trained without expert intervention by observing the execution of random commands. We demonstrate our method on a diverse set of robot manipulators that vary in actuation, materials, fabrication, and cost. Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot. Because it enables robot control using a generic camera as the only sensor, we anticipate that our work will broaden the design space of robotic systems and serve as a starting point for lowering the barrier to robotic automation.