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Tactile Dexterity


PhD Student @ MIT with Alberto Rodriguez

B.Eng., and M.Eng. @ McGill with James R. Forbes

Github · G. Scholar · Linkedin

Recent Work

Research Projects

My primary research objective is to enable reactive robotic manipulation. The ability to perceive the environment and react to unanticipated events is of paramount importance to deploy robots that are to interact with the physical world, which is difficult to observe and predict. Whereas humans effectively process and react to sensing information, robot manipulation skills are most often programmed in an open-loop fashion, incapable of correcting their motion based on detected errors. My research aims to enable robots skilled at i) monitoring the execution of a task, and ii) using sensed information in real-time for closed-loop control.

Hybrid Feedback Control For Contact Interactions


We investigate investigates real-time control strategies for dynamical systems that involve frictional contact interactions. Hybridness and underactuation are key characteristics of these systems that complicate the design of feedback controllers. Our results show that a Model Predictive Control approach used in tandem with integer programming offers a powerful solution to capture the dynamic constraints associated with the friction cone as well as the hybrid nature of contact.

WAFR 2016 ICRA 2018

Data-Efficient Control for Planar Manipulation


This paper explores the data-complexity required to control manipulation tasks with a model-based approach, where the model is learned from data. We employ this methodology to the problem of pushing an object on a planar surface, and find that we can design effective control policies with small data requirements (less than $10$ datapoints) while achieving accurate closed-loop performance.

CoRL 2018

Autonomous Grasping


During the period 2015-2017, I participated in the Amazon Robotics Challenge (ARC) as part of team MIT-Princeton. The challenge involved developing a robotic system capable of recognizing and grasping novel objects in cluttered environments. Our approach made use of an object agnostic deep learning framework that directly maps visual observations to planned robot motion. This work has led to a 1st place in the 2017 stowing task challenge as well as a 2018 Amazon Best System Paper Award.

ICRA 2018

Tactile Regrasping


In this project, we develop tactile reflexes, giving robots the ability to make grasp adjustments immediately upon making contact with an object. Tactile information is used to assess in real-time the quality of a grasp and predict failures preemptively and to design a controller able to perform local grasp adjustments, where grasp are improved directly upon making initial touch with an object.

IROS 2018