Annan Zhang

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

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I am a PhD candidate at the MIT Computer Science & Artificial Intelligence Lab (CSAIL), advised by Daniela Rus. My research lies at the intersection of robotics, materials, and machine learning. I aim to develop physically intelligent robots by leveraging advanced manufacturing techniques and data-driven methods for sensing, modeling, and control.

My work has been published in top academic journals like Nature and Science Advances and at leading robotics venues like IJRR, ICRA, IROS, CoRL, and RA-L.

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 Zürich. 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. I also interned at Symbotic, a robotic warehouse automation company, where I developed computer vision models for a collision avoidance system.

In my free time, I enjoy reading and traveling.

(* indicates equal contribution)

We present a passive robotic fingertip made from a 3D-printed elastomer lattice with embedded air channels for robust tactile sensing. The single-material design supports displacement and force estimation, compliant control, tactile exploration, and withstands high-impact and fatigue testing.

We introduce the Neural Jacobian Field, an architecture that learns to control robots by observing the execution of random commands. Our method makes no assumptions about the robots' materials, actuation, or sensing, and enables self-modeling and visuomotor control of unknown robots.

We present a dual-arm robotic system that can autonomously plan and execute multi-step assembly of complex objects. By combining efficient global planning with robust local control through reinforcement learning, our system achieves successful real-world assembly without requiring human demonstrations or domain knowledge.

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.

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.

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.

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.

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.

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.

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.

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.

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.

• Best Paper Award (top 2 of 738 submissions) at CoRL 2025, the premier conference on machine learning for robotics
• Best Presentation Award at the ICRA 2025 Workshop on Mechanical Intelligence
• Best Paper Award (top 1 of 59 submissions) at WAFR 2022, the premier conference on algorithms for robotics
• ETH Excellence Scholarship, a prestigious full-ride scholarship awarded by the ETH Rector to pursue a master's at ETH
• ETH Outstanding Bachelor Award for graduating as valedictorian of the mechanical engineering class of 2019
• ETH Outstanding Bachelor Award for finishing 2nd out of 543 in the selective first-year exams

Last updated: October 2025

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