Publications

This list includes publications from the PI’s time at the Harvard Agile Robotics and Edge Computing Labs.

Preprints

(2024). Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC.

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(2024). Materiality and Risk in the Age of Pervasive AI Sensors.

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(2023). Datasheets for Machine Learning Sensors.

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Journal Articles

(2022). Widening Access to Applied Machine Learning with TinyML. In HDSR 2022.

PDF DOI Course Main Page TinyML Open Education Initiative MOOC Website MOOC Course Materials

(2021). The Role of Compute in Autonomous Aerial Vehicles. In TOCS 2021.

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(2021). Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA. In RA-L and at ICRA 2021.

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Conference Proceedings

(2024). Invited: The Magnificent Seven Challenges and Opportunities in Domain-Specific Accelerator Design for Autonomous Systems. In DAC 2024.

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(2024). Parallel Optimization for Robotics: An Undergraduate Introduction to GPU Parallel Programming and Numerical Optimization Research. In IPDPSW 2024.

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(2024). TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers. In ICRA 2024.
Best Paper in Automation at IEEE ICRA 2024
Finalist for Best Conference Paper at IEEE ICRA 2024
Finalist for Best Student Paper at IEEE ICRA 2024

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(2024). Symmetric Stair Preconditioning of Linear Systems for Parallel Trajectory Optimization. In ICRA 2024.

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(2024). RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance. In ICRA 2024.
Best Poster Award for preliminary results at the Workshop on Methods for Objective Comparison of Results in Intelligent Robotics Research at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS)

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(2024). MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU. In ICRA 2024.
Best Poster Award at the 2024 IEEE TC on Model Based Optimization for Robotics Virtual Poster Session

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(2024). Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning. In ICRA 2024.

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(2024). TinyML4D: Scaling Embedded Machine Learning Education in the Developing World. In AAAI IDAIE 2024.

DOI TinyML4D Website Symposium Website

(2023). RoboShape: Using Topology Patterns to Scalably and Flexibly Deploy Accelerators Across Robots. In ISCA 2023.

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(2023). Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion. In ICRA 2023.

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(2022). Stop Reinventing the Wheel! Promoting Community Software in Computing Education. In ITiCSE-WGR 2022.

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(2022). RobotCore: An Open Architecture for Hardware Acceleration in ROS 2. In IROS 2022.

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(2022). Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots. In AICAS 2022.

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(2022). GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients. In ICRA 2022.

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(2021). RoboRun: A Robot Runtime to Exploit Spatial Heterogeneity. In DAC 2021.

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(2021). Robomorphic Computing: A Design Methodology for Domain-Specific Accelerators Parameterized by Robot Morphology. In ASPLOS 2021.
IEEE Micro Top Picks 2022 Honorable Mention

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(2019). Application of Approximate Matrix Multiplication to Neural Networks and Distributed SLAM. In HPEC 2019.

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(2018). A Performance Analysis of Parallel Differential Dynamic Programming on a GPU. In WAFR 2018.

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(2017). Project-based, collaborative, algorithmic robotics for high school students: Programming self-driving race cars at MIT. In ISEC 2017.

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(2017). Constrained Unscented Dynamic Programming. In IROS 2017.

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Magazine Articles and Columns

(2024). Underrepresentation of Women in Robotics Research. In RAM 2024.

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(2024). AI in the Developing World: How ‘Tiny Machine Learning’ can have a Big Impact. In The Conversation.

Article

(2023). Machine Learning Sensors: A Design Paradigm for the Future of Intelligent Sensors. In CACM 2023.

DOI Website Full Technical Report

(2023). Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers. In CACM 2023.

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Posters / Abstracts / Other

(2024). Optimizing at All Scales: Edge (Non)linear Model Predictive Control from MCUs to GPUs. In Frontiers of Optimization at RSS 2024.

PDF Poster Workshop Website TinyMPC Website MPCGPU Website

(2023). Can Large Language Models Reduce the Barriers to Entry for High School Robotics?. In R4L at RO-MAN 2023.

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(2023). Tiny Robot Learning: Expanding Access to Edge ML as a Step Toward Accessible Robotics. In Workshop on Lowering Barriers for Robotics Research at RSS 2023.

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(2023). Bridging the Digital Divide: the Promising Impact of TinyML for Developing Countries. In UN STI Forum 2023.

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(2022). Leveraging Community Software in CS Education to Avoid Reinventing the Wheel. In ITiCSE 2022.

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(2022). Closing the Sim-to-Real Gap for Ultra-Low-Cost, Resource-Constrained, Quadruped Robot Platforms. In Workshop on Closing the Sim2Real Gap at RSS 2022.

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(2022). TinyML: Applied AI for Development. In UN STI Forum 2022.

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(2022). TinyMLedu: The Tiny Machine Learning Open Education Initiative. In SIGCSE 2022.

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(2019). Realtime Model Predictive Control using Parallel DDP on a GPU. In Online Optimal Control Workshop at ICRA 2019.

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Dissertations and Theses

(2022). Teaching Embedded Systems Programming. Harvard University Bachelors of Arts Thesis.

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(2022). GPU Acceleration for Real-time, Whole-body, Nonlinear Model Predictive Control. Harvard University PhD Dissertation.

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(2020). Reinforcement Learning to Enable Robust Robotic Model Predictive Control. Harvard University Bachelors of Arts Thesis.

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(2018). Parallel and Constrained Differential Dynamic Programming for Model Predictive Control. Harvard University Masters of Engineering Thesis.

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