Brief Research Overview  

Robot learning is a field of artificial intelligence focused on algorithms that allow robots to learn to perform tasks more intelligently. It is currently receiving significant attention from the scientific community. The SMART Lab is focusing on advanced deep learning and deep reinforcement learning methods with the goal of improving the practicality of these methods through learning from and more flexible interaction with humans. We are specifically studying cognitive computing methods to improve a robot's decision-making ability by modeling and learning the fast and accurate decision-making abilities of humans, and recognition methods to enable robots to recognize and judge the identity of an object/scene in real-time even in dynamic environments and with limited information.

You can learn more about our current and past research on robot learning below.

Socially-Aware Robot Navigation (2021 - Present)


Description: Socially aware robot navigation, in which a robot must optimize its trajectory to maintain comfortable and compliant spatial interactions with humans while also reaching its goal without collisions, is a fundamental but challenging task in the context of human-robot interaction. While existing learning-based methods have performed better than model-based ones, they still have drawbacks: reinforcement learning relies on handcrafted rewards that may not effectively quantify broad social compliance and can lead to reward exploitation problems, and inverse reinforcement learning requires expensive human demonstrations. The SMART Lab investigates various practical and theoretical robot learning topics in the context of robot navigation. For example, we recently proposed a feedback-efficient active preference learning (FAPL) approach for socially aware robot navigation, which translates human comfort and expectation into a reward model that guides the robot agent to explore latent aspects of social compliance. The proposed method improved the efficiency of human feedback and samples through the use of hybrid experiential learning, and we evaluated the benefits of the robot behaviors learned from FAPL through extensive experiments.

Grant: NSF
People: Ruiqi Wang, Weizheng Wang
Project Website:

Selected Publications:

  • Ruiqi Wang, Weizheng Wang, and Byung-Cheol Min, "Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation", 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 23-27, 2022. Paper Link, Video Link, GitHub Link
Learning-based Robot Recognition (2017 - Present)


Description:The SMART Lab is researching learning-based robot recognition technology to enable robots to recognize and identify objects/scenes in real-time with the same ease as humans, even in dynamic environments and with limited information. We aim to apply our research and developments to a variety of applications, including the navigation of autonomous robots/cars in dynamic environments, the detection of malware/cyberattacks, object classification and reconstruction, the prediction of the cognitive and affective states of humans, and the allocation of workloads within human-robot teams. For example, we developed a system in which a mobile robot autonomously navigates an unknown environment through simultaneous localization and mapping (SLAM) and uses a tapping mechanism to identify objects and materials in the environment. The robot taps an object with a linear solenoid and uses a microphone to measure the resulting sound, allowing it to identify the object and material. We used convolutional neural networks (CNNs) to develop the associated tapping-based material classification system.

Grants: NSF, Purdue University
People: Wonse Jo, Shyam Sundar Kannan, Go-Eum Cha, Vishnunandan Venkatesh, Ruiqi Wang

Selected Publications:

  • Su Sun and Byung-Cheol Min, "Active Tapping via Gaussian Process for Efficient Unknown Object Surface Reconstruction", 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on RoboTac 2021: New Advances in Tactile Sensation, Interactive Perception, Control, and Learning. A Soft Robotic Perspective on Grasp, Manipulation, & HRI, Prague, Czech Republic, Sep 27 – Oct 1, 2021. Paper Link
  • Shyam Sundar Kannan, Wonse Jo, Ramviyas Parasuraman, and Byung-Cheol Min, "Material Mapping in Unknown Environments using Tapping Sound", 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Las Vegas, NV, USA, 25-29 October, 2020. Paper Link, Video Link
Application Offloading Problem (2018 - 22)


Description: Robots come with a variety of computing capabilities, and running computationally-intensive applications on robots can be challenging due to their limited onboard computing, storage, and power capabilities. Cloud computing, on the other hand, provides on-demand computing capabilities, making it a potential solution for overcoming these resource constraints. However, effectively offloading tasks requires an application solution that does not underutilize the robot's own computational capabilities and makes decisions based on cost parameters such as latency and CPU availability. In this research, we address the application offloading problem: how to design an efficient offloading framework and algorithm that optimally uses a robot's limited onboard capabilities and quickly reaches a consensus on when to offload without any prior knowledge of the application. Recently, we developed a predictive algorithm to predict the execution time of an application under both cloud and onboard computation, based on the size of the application's input data. This algorithm is designed for online learning, meaning it can be trained after the application has been initiated. In addition, we formulated the offloading problem as a Markovian decision process and developed a deep reinforcement learning-based Deep Q-network (DQN) approach.

Grants: Purdue University
People: Manoj Penmetcha , Shyam Sundar Kannan

Selected Publications:

  • Manoj Penmetcha and Byung-Cheol Min, "A Deep Reinforcement Learning-based Dynamic Computational Offloading Method for Cloud Robotics", IEEE Access, Vol. 9, pp. 60265-60279, 2021. Paper Link, Video Link
  • Manoj Penmetcha, Shyam Sundar Kannan, and Byung-Cheol Min, "A Predictive Application Offloading Algorithm using Small Datasets for Cloud Robotics", 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Virtual, Melbourne, Australia, 17-20 October, 2021. Paper Link, Video Link