Contact-Aware Manipulation

In hybrid force-velocity control, the robot can use velocity control in some directions to follow a trajectory, while performing force control in other directions to maintain contacts with the environment regardless of positional errors. We call this way of executing a trajectory hybrid servoing. We propose an algorithm to compute hybrid force-velocity control actions for hybrid servoing. We quantify the robustness of a control action and make trade-offs between different requirements by formulating the control synthesis as optimization problems. Our method can efficiently compute the dimensions,directions and magnitudes of force and velocity controls. We demonstrated by experiments the effectiveness of our method in several contact-rich manipulation tasks.


Robust Execution of Contact-Rich Motion Plans by Hybrid Force-Velocity Control 
Yifan Hou and Matthew T. Mason 

Conference Paper, Proceedings of IEEE International Conference on Robotics and Automation (ICRA), May, 2019


Selected Publications

Exact Bounds on the Contact Driven Motion of a Sliding Object, With Applications to Robotic Pulling 
Eric HuangAnkit Bhatia, Byron Boots and Matthew Mason 

Conference Paper, Robotics: Science and Systems, July, 2017 

Pushing revisited: Differential flatness, trajectory planning and stabilization 
Jiaji Zhou and Matthew T. Mason 

Conference Paper, ISRR 2017 : The 18th International Symposium on Robotics Research, December, 2017 

A Probabilistic Planning Framework for Planar Grasping Under Uncertainty 
Jiaji Zhou, Robert Paolini, Aaron M. JohnsonJ. Andrew (Drew) Bagnell and Matthew T. Mason 

Conference Paper, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017),September, 2017 

A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application 
Jiaji Zhou, Robert Paolini, J. Andrew (Drew) Bagnell and Matthew T. Mason 

Conference Paper, International Conference on Robotics and Automation (ICRA) 2016, May, 2016 


This work is a study of 2D manipulation without sensing and planning, by exploring the effects of unplanned randomized action sequences on 2D object pose uncertainty. Our approach follows the work of Erdmann and Mason’s sensorless reorienting of an object into a completely determined pose, regardless of its initial pose. While Erdmann and Mason proposed a method using Newtonian mechanics, this paper shows that under some circumstances, a long enough sequence of random actions will also converge toward a determined final pose of the object. This is verified through several simulation and real robot experiments where randomized action sequences are shown to reduce entropy of the object pose distribution. The effects of varying object shapes, action sequences, and surface friction are also explored.

Selected Publications

Sensorless Pose Determination using Randomized Action Sequences 
Pragna Mannam, Alexander Volkov Volkov, Jr., Robert Paolini, Gregory Chirikjian and Matthew T. Mason 

Journal Article, Entropy, Vol. 21, No. 2, pp. 154, February, 2019