Research

Dexterous Manipulation for Robotic Assembly

With advancements in sensing, actuation, and mechanical design, robots are becoming increasingly capable of performing complex and high-precision tasks, ranging from autonomous driving in urban areas to handling packages in fulfillment centers. The control and planning of robot motion in real-world environments demand high-fidelity models to simulate how the robot's movements affect physical objects before taking action. Many established approaches in robotics rely on mathematical models due to their analytical tractability. However, as robotic systems become more complex and are required to interact with physical environments, we encounter limitations in finding analytically tractable models. Thus, we must turn to new models that harness the computational power embedded in these systems. To address the challenges of robot operation in physical environments, we propose the concept of building computational models using physics engines and designing feedback algorithms to autotune model parameters (mass, inertia, friction of objects) whenever the robot detects disparities between the simulation and its real-world experience. As part of our key applications, we plan to use this new model to train robotic manipulators for tasks in assembly lines and research laboratories that are labor-intensive and require dexterous skills.

C. Shu, A. Kim, and S. Park, "Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning," Under review (preprint available at link).

Networked Robots for Monitoring Underwater Habitats

support from KAUST Red Sea Research Center / Raquel S. Peixoto (KAUST)

Coral reefs, surrounded by vibrant marine life, constitute rich and diverse habitats. Given their ecological and economic significance, continuous monitoring of their key areas is paramount for marine biology research, economic development, and environmental protection. The extended survey and observation of vast marine environments are labor-intensive, necessitating technological advancements. While many efforts have been made in research and development to create autonomous robots capable of surveying and collecting data in remote areas inaccessible to human divers or requiring long-term and repeated monitoring, technical challenges remain. These challenges include implementing navigation strategies for individual underwater robots to operate in close proximity to marine life for extended periods while ensuring safety and non-interference with natural habitats. Additionally, the severe signal attenuation underwater poses technical obstacles in information exchange and achieving a high degree of coordination among multiple robots tasked with surveying large-scale coral reef environments. The project explores learning-based planning and control approaches for individual robots to acquire safe navigation strategies and adaptively apply them in response to environmental conditions, such as light conditions and current speeds. Furthermore, drawing from theoretical studies in multi-agent coordination, we aim to investigate how the robots can adopt optimal team strategies while working under the constraints of limited information exchange.

Multi-Robot Perception, Manipulation, and Learning

joint work with Naomi Leonard (Princeton), Naveen Verma (Princeton), Szymon Rusinkiewicz (Princeton), Thomas Funkhouser (Princeton), and Anirudha Majumdar (Princeton)

An important question in multi-agent system research is "how to synergize individual agents’ capabilities in sensing, actuation, and communication?" This research aims at discovering an effective multi-agent decision-making model for a robotic team to learn and attain optimal team strategies in dynamic and resource-constrained environments. We apply outcomes of the research to conceiving a heterogenous robotic team that can be used to address real societal problems such as waste collection/removal.

S. Park, Y. Desmond Zhong, and N. E. Leonard, "Multi-Robot Task Allocation Games in Dynamically Changing Environments, " IEEE International Conference on Robotics and Automation (ICRA), June 2021 (link).

Social Trajectory Planning for Autonomous Surface Vessels

joint work with Michal Cap (TU Delft), Javier Alonso-Mora (TU Delft), Carlo Ratti (MIT), and Daniela Rus (MIT)

We imagine, in the future, a fleet of autonomous vessels to be employed as a new means of transportation through city's waterways. To ensure safety in vessel navigation, we need to understand the navigation behaviors of human-operated vessels and allow autonomous vessels to learn those behaviors. We propose a new approach called "Social Trajectory Planning" to address the key challenge in guaranteeing safety in canal navigation. Through validation using vessel trajectory data set, we demonstrate that Social Trajectory Planning reduces the chances of vessel-to-vessel collision and improves the predictability of autonomous vessels' movement.

S. Park, M. Cap, J. Alonso-Mora, C. Ratti, and D. Rus, “Social Trajectory Planning for Urban Autonomous Surface Vessels,” IEEE Transactions on Robotics, Oct. 2020 (link).

Higher-Order Learning in Population Games

joint work with Nuno Martins (UMD) and Jeff S. Shamma (KAUST)

This work formalizes decision-making models and proposes analytical methods to establish stability in strategic interactions among a vast number of decision-making agents. In this context, evolutionary dynamics models describe how the portions of agents adopting each available strategy evolve in response to the payoff ascribed by a payoff dynamics model. Motivated by its importance in developing effective multi-agent decision-making algorithms in a wide range of engineering applications, this work describes a new class of payoff dynamics models and analytical methods that hinge on passivity-based techniques to characterize the stability of evolutionary dynamics model under payoff dynamics models.

S. Park, J. S. Shamma, and N. C. Martins, "From Population Games to Payoff Dynamics Models: A Passivity-Based Approach," IEEE Conference on Decision and Control (CDC), Dec. 2019 (tutorial session).
S. Park, N. C. Martins, and J. S. Shamma, "Payoff Dynamics Model and Evolutionary Dynamics Model: Feedback and Convergence to Equilibria," arXiv:1903.02018.

Roboat: Fleet of Reconfigurable Robotic Vessels

joint work with Daniela Rus (MIT) and Carlo Ratti (MIT)

At the intersection of urban mobility and self-reconfigurable modular robotics, this research project seeks a new approach for design and control of a fleet of autonomous surface vessels (ASV) that can navigate and self-assemble into dynamic infrastructures in city's waterway. We conceive a heterogeneous team of reconfigurable ASVs and develop multi-vessel control and planning algorithms that enable the fleet's core capabilities for navigation and self-assembly in canal environments. We envision this fleet of autonomous vessels will serve as a new means of transportation and on-demand infrastructure in the near future. Check out Roboat website for more.

B. Gheneti, S. Park, R. Kelly, D. Meyers, P. Leoni, C. Ratti, and D. Rus, "Trajectory Planning for the Shapeshifting of Autonomous Surface Vessels," 2nd IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS' 19), Aug. 2019.
S. Park, E. Kayacan, C. Ratti, and D. Rus, "Coordinated Control of a Reconfigurable Multi-Vessel Platform: Robust Control Approach", IEEE International Conference on Robotics and Automation (ICRA), May 2019.

Underworlds: Smart Sewage Infrastructure for Urban Epidemiology

joint work with Daniela Rus (MIT), Carlo Ratti (MIT), and Eric Alm (MIT)

Underworlds proposes to develop a human health census by sampling the “urban gut” through collecting and analyzing sewage samples at high-level of spatial and temporal resolutions. We design a smart sewage infrastructure consisting of networked sensing/sampling devices, a computational approach to collect most informative sewage samples (subject to budget constraints), and data analysis/visualization pipelines. Our team has been successfully deployed Underworlds around the globe. Check out Underworlds website for more.

E. D Evans, C. Dai, S. Isazadeh, S. Park, C. Ratti, and E. J. Alm, "Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites," PLOS Computational Biology, June 2020 (link).
S. Park, C. Ratti, and D. Rus, "Adaptive Sensor Selection for Monitoring Stochastic Processes," IEEE Conference on Decision and Control (CDC), Dec. 2018.

Animal-borne Wireless Camera Network: Remote Imaging of Community Ecology

joint work with Nuno Martins (UMD), Naomi Leonard (Princeton), and Kyler Abernathy (NGS)

This collaborative research aims at designing, constructing, and field-testing a standalone networked animal‐borne monitoring system conceived to study community ecology remotely. The system is designed to use information exchange across the network for the devices to jointly decide without supervision when and how to use each sensing modality. The significance of the system is in its capability to be programmed to selectively document events of interactions in animal groups and its ability to operate for an extended period of time in natural habitats of study animals. Through system deployment in Gorongosa National Park (Mozambique), our team validated its long-term data collection capability and collected animal behavioral data which have been used as important resources in ecology studies.

S. Park, K. H. Aschenbach, M. Ahmed, W. Scott, N. E. Leonard, K. Abernathy, G. Marshall, M. Shepard, and N. C. Martins, “Animal-Borne Wireless Network: Remote Imaging of Community Ecology,” Journal of Field Robotics, July 2019 (link, multimedia).

Distributed State Estimation for Omniscience

Motivated by challenges in estimation over a network of sensing devices and its importance in engineering applications such as connected-vehicle control and environmental monitoring, this work proposes a distributed estimation algorithm for a network of agents to estimate a state vector of a large-scale dynamical system over a communication-constrained network. The key findings of the work are that we characterize exact conditions under which every agent asymptotically recovers the system’s entire state vector (with exponential recovery rate) – state omniscience – and the complexity of the algorithm depends linearly on the size of the system – scalability. The work becomes a central piece in designing a distributed sensor fusion algorithm for the animal-borne wireless camera network to effectively document group behavior of wild animals.

S. Park and N. C. Martins, "Design of Distributed LTI Observers for State Omniscience," IEEE Transactions on Automatic Control, Vol.62, No.2, Feb. 2017 (link).