Sangwoon Kim

I am interested in full-time employment starting in 2024 Summer (flexible) upon completing my Ph.D. at MIT. Please let me know if you are interested in working with me.

I am a Ph.D. student in the MechE department at MIT, advised by Professor Alberto Rodriguez. I am interested in robot manipulation and automation.

Previously, I received my M.S. in Mechanical Engineering from MIT, where I worked with Dr. Brian Anthony. I also received my B.S. in Mechanical and Aerospace Engineering from Seoul National University.

Keywords: Robot Manipulation, Reinforcement Learning, Tactile Sensing

CV / Email / Google Scholar / GitHub / LinkedIn / Work

selected publications

Simultaneous Tactile Estimation and Control of Extrinsic Contact

Sangwoon Kim, Devesh Jha, Diego Romeres, Parag Patre, and Alberto Rodriguez

In International Conference on Robotics and Automation (ICRA) 2023

Video Abs PDF Code Website

We propose a method that simultaneously estimates and controls extrinsic contact with tactile feedback. The method enables challenging manipulation tasks that require controlling light forces and accurate motions in contact, such as balancing an unknown object on a thin rod standing upright. A factor graph-based framework fuses a sequence of tactile and kinematic measurements to estimate and control the interaction between gripper-object-environment, including the location and wrench at the extrinsic contact between the grasped object and the environment and the grasp wrench transferred from the gripper to the object. The same framework simultaneously plans the gripper motions that make it possible to estimate the state while satisfying regularizing control objectives to prevent slip, such as minimizing the grasp wrench and minimizing frictional force at the extrinsic contact. We show results with sub-millimeter contact localization error and good slip prevention even on slippery environments, for multiple contact formations (point, line, patch contact) and transitions between them. See supplementary video and results at https://sites.google.com/view/sim-tact.
Active extrinsic contact sensing: Application to general peg-in-hole insertion

Sangwoon Kim, and Alberto Rodriguez

In International Conference on Robotics and Automation (ICRA) 2022

Video Abs PDF Code Website

We propose a method that actively estimates contact location between a grasped rigid object and its environment and uses this as input to a peg-in-hole insertion policy. An estimation model and an active tactile feedback controller work collaboratively to estimate the external contacts accurately. The controller helps the estimation model get a better estimate by regulating a consistent contact mode. The better estimation makes it easier for the controller to regulate the contact. We then train an object-agnostic insertion policy that learns to use the series of contact estimates to guide the insertion of an unseen peg into a hole. In contrast with previous works that learn a policy directly from tactile signals, since this policy is in contact configuration space, it can be learned directly in simulation. Lastly, we demonstrate and evaluate the active extrinsic contact line estimation and the trained insertion policy together in a real experiment. We show that the proposed method inserts various-shaped test objects with higher success rates and fewer insertion attempts than previous work with end-to-end approaches.
Dynamic control of a fiber manufacturing process using deep reinforcement learning

Sangwoon Kim, David Donghyun Kim, and Brian W Anthony

IEEE/ASME Transactions on Mechatronics 2021

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This paper presents a model-free deep reinforcement learning (DRL) approach for controlling a fiber drawing system. The custom DRL-based control system predictively regulates fiber diameter and produces a fiber with a desired, constant or non-constant, diameter trajectory, i.e. diameter variation along the fiber length. Physical models of the system are not used. The system was trained and tested on a compact fiber drawing system, which has non-linear delayed dynamics and stochastic behaviors. For a reference trajectory with random step changes, after 1 hour of training, the DRL controller showed the same root mean squared error (RMSE) as an optimized PI controller; after 3 hours of training, it achieved the performance of a quadratic dynamic matrix controller (QDMC). While the PI feedback controller showed 3.5 seconds of time lag in a step response, the DRL controller showed less than a second of time lag. Controller performance tests on trajectories not used in the training process are conducted; for a sine sweep reference trajectory, the DRL controller maintained an RMSE under 40 micron up to a frequency of 45 mHz, compared to 25 mHz for QDMC.