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 :



#IJCAI2025 distinguished paper: Combining MORL with restraining bolts to learn normative behaviour

and   04 Sep 2025
The authors introduce a framework for guiding reinforcement learning agents to comply with social, legal, and ethical norms.

Researchers are teaching robots to walk on Mars from the sand of New Mexico

  02 Sep 2025
Researchers are closer to equipping a dog-like robot to conduct science on the surface of Mars

Engineering fantasy into reality

  26 Aug 2025
PhD student Erik Ballesteros is building “Doc Ock” arms for future astronauts.

RoboCup@Work League: Interview with Christoph Steup

and   22 Aug 2025
Find out more about the RoboCup League focussed on industrial production systems.

Interview with Haimin Hu: Game-theoretic integration of safety, interaction and learning for human-centered autonomy

and   21 Aug 2025
Hear from Haimin in the latest in our series featuring the 2025 AAAI / ACM SIGAI Doctoral Consortium participants.

AIhub coffee corner: Agentic AI

  15 Aug 2025
The AIhub coffee corner captures the musings of AI experts over a short conversation.

Interview with Kate Candon: Leveraging explicit and implicit feedback in human-robot interactions

and   25 Jul 2025
Hear from PhD student Kate about her work on human-robot interactions.

#RoboCup2025: social media round-up part 2

  24 Jul 2025
Find out what participants got up to during the second half of RoboCup2025 in Salvador, Brazil.



 

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