Soft robot arm shakes objects to find hidden weight details
Shake to Learn: Dynamic Interrogation of Hidden Object Physics for Robotic Manipulation with Physical Reservoir Computing
Robotics
Summary
Many things robots pick up hide important details, like where their weight is inside. The authors show that by shaking an object with a soft robot arm and watching how it moves, a robot can guess hidden properties like the center of mass. They use a special kind of soft robot that acts like a computer processing the shaking response, and then a simple math step decodes the hidden info. This helps the robot handle objects better by knowing more about what it’s holding.
What this means in practice
- •For warehouse automation teams: Improve robots’ ability to identify and grasp unknown packages by shaking to infer hidden weight distribution for safer handling.
- •For manufacturing robotics engineers: Enable robots to better regrasp or manipulate sealed components by decoding internal physics from dynamic shaking responses.
Authors
Wen Sin Lor, Jun Wang, Suyi Li
Abstract
Many physical properties relevant to robotic manipulation are hidden from vision. A sealed object, for example, may reveal little about its center of mass (COM) or internal contents until it is lifted, shaken, or otherwise dynamically perturbed. This study shows that such interactions can enable a new modality of robotic perception and learning, in which interaction-induced dynamic responses are used to infer object physics that is inaccessible to conventional sensing. We implement this idea using an origami-inspired soft robotic arm that functions as a physical reservoir computer. After grasping an object, the arm is excited by a fixed shaking input at its base, and the resulting ringdown response is recorded through either camera tracking or embedded sensors. Because the input is held constant across trials, hidden object properties, such as the COM position, are encoded through their effect on the dynamics of the coupled robot-object system. A lightweight linear readout can then decode these dynamics to recover interpretable information about the hidden object physics. Using this framework, the soft robotic arm reservoir completed three tasks of increasing difficulty: inferring the orientation of the object's hidden COM, inferring the COM distance from the grasp point, and using the inferred COM information to guide a subsequent regrasp. We further develop a dynamic summary representation of the ringdown response that improves prediction accuracy. Together, these results establish shake-to-learn mechanical interrogation as a promising strategy for robotic systems to convert brief physical interactions into actionable cues about hidden object properties for downstream manipulation.