profile photo

Annan Zhang

PhD Student
Robotics & Artificial Intelligence
Massachusetts Institute of Technology (MIT)

Email  /  Google Scholar  /  LinkedIn  /  GitHub

About Me

I am a PhD student at the MIT Computer Science & Artificial Intelligence Lab (CSAIL), advised by Daniela Rus. My research interests lie at the intersection of robotics, materials, and machine learning. My goal is to combine compliance and machine learning to develop physically intelligent robots.

Previously, I earned a master's degree in electrical engineering and computer science from MIT and a master's and a bachelor's degree in mechanical engineering from ETH Z├╝rich. At ETH, I worked with Dirk Mohr on machine learning-based modeling of mechanical material behavior.

I spent a summer working at the asset management company Vanguard, where I explored the use of artificial intelligence and large language models in finance.

In my free time, I enjoy running, reading, and traveling. I always appreciate book recommendations. Add me on Goodreads!


(* indicates equal contribution)

Embedded Air Channels Transform Soft Lattices into Sensorized Grippers
Annan Zhang*, Lillian Chin*, Daniel L. Tong, Daniela Rus
Accepted at the 2024 IEEE International Conference on Robotics and Automation (ICRA)
Preprint  /  BibTeX

We present a robotic gripper with integrated sensing made from 3D printed elastomer lattices with embedded air channels. Our method simplifies the fabrication process for sensorized grippers and provides sufficient sensor resolutions to reason about grasp location and grasp forces.

Real-Time Grocery Packing by Integrating Vision, Tactile Sensing, and Soft Fingers
Valerie K. Chen*, Lillian Chin*, Jeana Choi*, Annan Zhang*, Daniela Rus
Accepted at the 2024 IEEE International Conference on Soft Robotics (RoboSoft)
Preprint  /  BibTeX

We present a grocery packing robot that can pack a stream of unknown objects on a conveyor belt. By integrating multiple sensing modalities, our system estimates object size and stiffness to avoid damaging packings.

Machine Learning Best Practices for Soft Robot Proprioception
Annan Zhang*, Tsun-Hsuan Wang*, Ryan L. Truby, Lillian Chin, Daniela Rus
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Paper  /  BibTeX

Based on experiments on two large soft robotics datasets, we derive best practices for training neural networks that map sensor signals to soft robot shape.

Certified Polyhedral Decompositions of Collision-Free Configuration Space
Hongkai Dai*, Alexandre Amice*, Peter Werner, Annan Zhang, Russ Tedrake
Accepted at The International Journal of Robotics Research (IJRR), 2023
Preprint  /  BibTeX

We present a method to generate large collision-free regions in configuration space for sampling- and optimization-based motion planning. We extend the theoretical framework in Amice et al. (2022) and generalize the algorithm to handle algebraic joints and non-polytopic geometries.

Vision-Based Sensing for Electrically-Driven Soft Actuators
Annan Zhang, Ryan L. Truby, Lillian Chin, Shuguang Li, Daniela Rus
IEEE Robotics and Automation Letters (RA-L), 2022
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Paper  /  BibTeX

We use cameras to record the interior of compliant electric actuators and train a neural network that maps the visual feedback to the actuator's tip pose. Our method presents a robust approach for sensorizing hollow-bodied actuators and provides accurate predictions in the presence of external disturbances.

Fluidic Innervation Sensorizes Structures from a Single Build Material
Ryan L. Truby*, Lillian Chin*, Annan Zhang, Daniela Rus
Science Advances, 2022
Paper  /  BibTeX  /  MIT News  /  Nature Reviews Materials

We embed air-filled channels within architected materials and measure the pressure change during deformation. Our method integrates programmed mechanical behavior, sensing, and actuation and enables sensorized structures for wearables and robotics from one single build material.

Finding and Optimizing Certified, Collision-Free Regions in Configuration Space for Robot Manipulators
Alexandre Amice*, Hongkai Dai*, Peter Werner, Annan Zhang, Russ Tedrake
2022 Workshop on the Algorithmic Foundations of Robotics (WAFR), Best Paper Award
Paper  /  BibTeX  /  Talk

We use convex optimization to generate regions in configuration space that are guaranteed to be collision-free. Our method scales to high-dimensional robot manipulators and paves the way for motion planning with verifiable non-collision.

Simulation and Fabrication of Soft Robots with Embedded Skeletons
James Bern, Fatemeh Zargarbashi, Annan Zhang, Josie Hughes, Daniela Rus
2022 IEEE International Conference on Robotics and Automation (ICRA)
Paper  /  BibTeX  /  Video

We present a pipeline to simulate and fabricate cable-driven soft robots with embedded skeletons. These hybrid soft-rigid robots combine the best of both worlds and simultaneously provide strength and robustness.

Using Neural Networks to Represent von Mises Plasticity with Isotropic Hardening
Annan Zhang, Dirk Mohr
International Journal of Plasticity, 2020
Paper  /  BibTeX

We demonstrate how a plasticity model widely used for ductile engineering materials can be learned by a neural network. We deploy the neural network in commercial finite element software and show its capability to correctly predict stresses from strains.

Teaching Fellow, 6.1210 Introduction to Algorithms (formerly 6.006), Spring 2024

Mentor, Graduate Application Assistance Program, 2021-Present
Paper Reviewer, IEEE Robotics and Automation Letters (RA-L), 2023-Present

Paper Reviewer, Soft Robotics (Journal), 2022-Present

Paper Reviewer, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

Paper Reviewer, IEEE International Conference on Soft Robotics (RoboSoft), 2022-2024
President, Board Member, MIT European Club, 2021-Present
Teaching Assistant, Physics I & Physics II, Summer 2019

Teaching Assistant, Engineering Materials and Production I, Fall 2017
Student Representative, ETH Engineering Student Association, 2016-2020

Board Member, ETH Engineering Student Association, Fall 2017


Last updated: February 2024