Robohub.org
 

Teaching robots the physics of sliding and pushing objects


by
16 June 2016



share this:

Robot learns to push object and identifies patch friction model. Source: YouTube

Robot learns to push object and identifies patch friction model. Source: YouTube



The Manipulation Lab at the CMU Robotics Institute proposes a computational model that relates an applied robot action to the resultant object motion. Their research won the Best Conference Paper Award at ICRA 2016.

Understanding the mechanics of manipulation is essential for robots to autonomously interact with the physical world. One of the common manipulation scenarios involves pushing objects in a plane subject to dry friction. We propose a planar friction (force-motion) model that relates an applied robot action to the resultant object motion.

The robot randomly pokes the object of known shape with a point finger to collect force-motion data. We then optimize a convex polynomial friction representation with physics-based constraints. Based on the representation, we demonstrate applications of stable pushing and dynamic sliding simulation.

The robot randomly pokes the object of known shape with a point finger to collect force-motion data. We then optimize a convex polynomial friction representation with physics-based constraints. Based on the representation, we demonstrate applications of stable pushing and dynamic sliding simulation.

The difficulty lies in that the contact between the object and supporting surface is an area-to-area contact with unknown pressure distribution. We don’t know which part of the area is supporting how much weight nor do we know the coefficient of friction. This makes object motion hard to predict. The key observation is the space of generalized friction force forms a convex set based on the principle of maximum dissipation (a generalized Coulomb’s friction law) [1]. The boundary of such set is termed as limit surface [2]. The geometry of such surface, albeit convex, can be complicated. Fortunately, we have shown that level sets of sum of squares convex polynomials turn out to be good geometric approximations. Another advantage is the model is very data-efficient, i.e., model identification only requires few force and velocity data collected by the robot pushing the object with a point finger. There are some additional nice provable properties of the models, and with these properties, we are able to perform applications including stable pushing and free sliding dynamics simulation.

[1] J. J. Moreau, “Unilateral contact and dry friction in finite freedom dynamics,” in Nonsmooth Mechanics and Applications, pp. 1–82, Springer, 1988.

[2] S. Goyal, A. Ruina, and J. Papadopoulos, “Planar sliding with dry friction. Part 1. Limit surface and moment function,” Wear, vol. 143, pp. 307–330, 1991.

Paper: A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application: Jiaji Zhou, Robert Paolini, James Bagnell, Matthew T. Mason

Read the award winning paper here



tags: ,


Jiaji Zhou is a PhD student in the Robotics Institute of Carnegie Mellon University.
Jiaji Zhou is a PhD student in the Robotics Institute of Carnegie Mellon University.





Related posts :



Meet the AI-powered robotic dog ready to help with emergency response

  07 Jan 2026
Built by Texas A&M engineering students, this four-legged robot could be a powerful ally in search-and-rescue missions.

MIT engineers design an aerial microrobot that can fly as fast as a bumblebee

  31 Dec 2025
With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.

Robohub highlights 2025

  29 Dec 2025
We take a look back at some of the interesting blog posts, interviews and podcasts that we've published over the course of the year.

The science of human touch – and why it’s so hard to replicate in robots

  24 Dec 2025
Trying to give robots a sense of touch forces us to confront just how astonishingly sophisticated human touch really is.

Bio-hybrid robots turn food waste into functional machines

  22 Dec 2025
EPFL scientists have integrated discarded crustacean shells into robotic devices, leveraging the strength and flexibility of natural materials for robotic applications.

Robot Talk Episode 138 – Robots in the environment, with Stefano Mintchev

  19 Dec 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Stefano Mintchev from ETH Zürich about robots to explore and monitor the natural environment.

Artificial tendons give muscle-powered robots a boost

  18 Dec 2025
The new design from MIT engineers could pump up many biohybrid builds.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


 












©2025.05 - Association for the Understanding of Artificial Intelligence