Haptic Identification of Objects using a Modular Soft Robotic Gripper

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This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements. A highly compliant hand allows for intrinsic robustness to grasping uncertainty, but the specific configuration of the hand and object is not known, leaving undetermined if a grasp was successful in picking up the right object. A soft finger was adapted and combined to form a three finger gripper that can easily be attached to existing robots, for example, to the wrist of the Baxter robot. Resistive bend sensors were added within each finger to provide a configuration estimate sufficient for distinguishing between a set of objects. With one data point from each finger, the object grasped by the gripper can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. This hand is a first step towards robust proprioceptive soft grasping.

This work will be presented at IROS 2015. 1st author.

Objects grasped by the soft gripper

• Lab: Distributed Robotics Lab
• Date: March 2015
• Paper Link: International Conference on Intelligent Robots and Systems

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Semantic Mapping

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This paper describes a framework that enables robots to efficiently learn human-centric models of their environment from natural language descriptions. Typical semantic mapping approaches are limited to augmenting metric maps with higher-level properties of the robot’s surroundings (e.g., place type, object locations) that can be inferred from the robot’s sensor data, but do not use this information to improve the metric map. The novelty of our algorithm lies in fusing high-level knowledge that people can uniquely provide through speech with metric information from the robot’s low-level sensor streams. Our method jointly estimates a hybrid metric, topological, and semantic representation of the environment. This semantic graph provides a common framework in which we integrate information that the user communicates (e.g., labels and spatial relations) with metric observations from low-level sensors. Our algorithm efficiently maintains a factored distribution over semantic graphs based upon the stream of natural language and low-level sensor information. We detail the means by which the framework incorporates knowledge conveyed by the user’s descriptions, including the ability to reason over expressions that reference yet unknown regions in the environment. We evaluate the algorithm’s ability to learn human-centric maps of several different environments and analyze the knowledge inferred from language and the utility of the learned maps. The results demonstrate that the incorporation of information from free-form descriptions increases the metric, topological and semantic accuracy of the recovered environment model.

This work was presented at RSS 2013 and was accepted to IJRR 2014. 3rd author.

• Lab: Robust Robotics Group
• Date: Summer 2012
• Papers: Robotics: Science and Systems 2013 , International Journal of Robotics Research 2014

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Internships

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Summer 2015: Mayfield Robotics , Robotics Intern

Summer 2014: SpaceX , Power Electronics Intern

Summer 2013: Google X, Software Engineering Intern on Project Loon

More details available at LinkedIn

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I worked on the M-Block modular robotics system. Contributions included updating a modular robotics simulator to work with the next generation robots, contributing to the firmware of the robot, testing and debugging, and designing printed circuit boards for communication between robots with IR leds and receivers, an accelerometer, a small processor, and an I2C link to the main processor.

• Video: First Rev M-Blocks
• Date: October 2013 - May 2014

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Leadership Experience

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MIT Fall Career Fair Committee - Day of Logistics Director

May 2014 - Fall 2014

Planned, organized, and managed day-of logistics for the fall career fair with 380 companies, over 4000 students, and $1M in revenue, including both the main fair and on-campus interviews.

MIT Eta Kappa Nu - Outreach Chair

May 2014 - May 2015

Events include a technical interview training, assertiveness training, negotiation workshop, command line workshop, and soldering workshop.

MIT Educational Studies Program - HSSP Director, Splash for Us Director, Junction Seminar Teacher, Splash Security Director

Fall 2011 - Spring 2015

The MIT Educational Studies Program runs various educational programs for middle, high-school, and college students, all with college teachers. I directed HSSP, an 8-week long program with classes every Saturday; founded and directed Splash for Us, where MIT students teach other MIT students; taught for Junction, a 6-week long intensive evening summer program; and ran security for Splash, a weekend where almost 3000 students take classes at MIT.

MIT Undergraduate Advisory Group in EECS (USAGE) - Committee Member

Fall 2011 - Spring 2015

Provide feedback and suggest initiatives to improve the undergraduate academic experience in EECS.

• MIT Fall Career Fair
• MIT Eta Kappa Nu
• MIT ESP
• EECS USAGE

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Coursework

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Relevant coursework includes:

Cognitive Robotics (Grad), Underactuated Robotics (Grad), How to Make Almost Anything (Grad), Robotics: Science and Systems, Introduction to Machine Learning, Planning Algorithms, Signals and Systems, Advanced Algorithms (Grad), Computational Cognitive Science, Circuits and Electronics, Theory of Computation (Grad), Mechanical Invention through Computation

• How to Make Almost Anything: Project Website
• Robotics: Science and Systems: Final Project Github Repository
• Cognitive Robotics: Final Project Github Repository

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