Publications

TRI Authors: Kuan-Hui Lee, Adrien Gaidon

All Authors: Karttikeya Mangalam, Ehsan Adeli, Kuan-Hui Lee, Adrien Gaidon, and Juan Carlos Niebles We tackle the problem of Human Locomotion Forecasting, a task for jointly predicting the spatial positions of several keypoints on human body in the near future under an egocentric setting. In contrast to the previous work that aims to solve either the task of pose prediction or trajectory forecasting in isolation, we propose a framework to unify these two problems and address the practically useful task of pedestrian locomotion prediction in the wild. Among the major challenges in solving this task is the scarcity of annotated egocentric video datasets with dense annotations for pose, depth, or egomotion. To surmount this difficulty, we use state-of-the-art models to generate (noisy) annotations and propose robust forecasting models that can learn from this noisy supervision. We present a method to disentangle the overall pedestrian motion into easier to learn subparts by utilizing a pose completion and a decomposition module. The completion module fills in the missing key-point annotations and the decomposition module breaks the cleaned locomotion down to global (trajectory) and local (pose keypoint movements). Further, with Quasi RNN as our backbone, we propose a novel hierarchical trajectory forecasting network that utilizes low-level vision domain specific signals like egomotion and depth to predict the global trajectory. Our method leads to state-of-the-art results for the prediction of human locomotion in the egocentric view.  Read more 

Citations: Mangalam, Karttikeya, Ehsan Adeli, Kuan-Hui Lee, Adrien Gaidon, and Juan Carlos Niebles. "Disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision." In The IEEE Winter Conference on Applications of Computer Vision, pp. 2784-2793. 2020.

TRI Authors: Patrick H. Herring, Muratahan Aykol

All Authors: Peter Attia, Aditya Grover, Norman Jin, Kristen Severson, Bryan Cheong, Jerry Liao, Michael H Chen, Nicholas Perkins, Zi Yang, Patrick H Herring, Muratahan Aykol, Stephen J Harris, Richard D Braatz, Stefano Ermon, William C Chueh Lithium-ion batteries are complex electrochemical devices that spans multiple time and length scales. Such complexity presents challenges when engineering batteries. On a scientific level, establishing a physical picture that spans atomic, particle and device scales require integration of many types of data, physics/chemistry, and equations. On an engineering level, given the degrees of freedom, design of experiments can involve daunting number of permutations. The Center for Data-Driven Design of Li-Ion Batteries (D3BATT) at MIT, Stanford and Purdue aims to address these challenges by integrating a wide range of experiments, modeling, and data analytic approaches. In this talk, I will highlight the example of optimizing extreme fast charging, that is, charging batteries under ten minutes. We combined machine-learning and optimal experimental design methods to rapidly identify an effective extreme fast charging method on a time scale that is otherwise not possible. Read More

Citation: Attia, Peter, Aditya Grover, Norman Jin, Kristen Severson, Bryan Cheong, Jerry Liao, Michael H. Chen et al. "Closed-Loop Optimization of Battery Fast Charging Procedures." In Meeting Abstracts, no. 2, pp. 227-227. The Electrochemical Society, 2019.