Publications

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

Preprints

(2023). TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers.

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(2023). Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning.

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(2023). RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance.

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(2023). MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU.

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(2023). Symmetric Stair Preconditioning of Linear Systems for Parallel Trajectory Optimization.

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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.

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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 Papers

(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). 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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(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

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

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

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Workshop Papers / Abstracts / Posters

(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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