Research

NRI: FND: The Robotic Rehab Gym: Specialized Co-Robot Trainers Working with Multiple Human Trainees for Optimal Learning Outcomes, sponsored by NSF

Overview: A robotic rehabilitation gym is defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, this project investigates automated systems that can dynamically assign patients to different robots within a session in order to optimize group rehabilitation outcome.

Adaptive Shared Autonomy for Assistive Robotic Manipulation

Overview: Shared autonomy promotes collaboration between humans and robots by integrating human input with autonomous control, allowing humans to perform tasks without the need for precise manual control. A primary problem in shared autonomy is how to determine the optimal balance of control between the user and robot. This problem is further complicated by the variability in human proficiency and preference when performing manipulation tasks. Addressing this issue requires systems to infer human skill levels, enabling dynamic adjustments to the level of assistance. Such adaptability ensures that assistance is neither overly intrusive for experienced users nor insufficient for novices. To address this challenge, this project investigates human-centric shared autonomy approaches that offers adaptive and personalized level of assistance.

Multi-Robot Coordinated Planning and Control

Overview: Multi-robot motion planning and control has been investigated for decades and is still an active research area due to the growing demand for both performance optimality and safety assurance. We developed learning-based methods and distributed model predictive control (DMPC) methods: 1) a gradient-based algorithm that leverages the alternating direction method of multipliers (ADMM) to decompose the team-level trajectory optimization into subproblems solved by individual robots. The algorithm also incorporates a discrete-time control barrier functions (CBF) as safety constraints to provide formal guarantee of collision avoidance; and 2) a sampling-based method that formulates multi-robot optimal control as probabilistic inference over graphical models and leverages belief propagation to achieve inference via distributed computation. We developed a distributed sampling-based model predictive control (MPC) algorithm based on the proposed framework, which obtains optimal controls via variational inference (VI).

Robot-Assisted Pedestrian Regulation: Learning optimal human-robot interaction (past project)

Overview: This project investigates an emerging application of assistive robots in pedestrian regulation. We propose a novel robot-assisted pedestrian regulation framework that utilizes the dynamic pedestrian-robot interaction during their collective movements. The insights of the effect of pedestrian-robot interaction on the pedestrian movements and the optimal robot motion for desired pedestrian regulation objectives are provided. Adaptive dynamic programming (ADP) algorithm and deep reinforcement learning algorithms are designed to learn optimal control of robot motion. The proposed adaptive learning framework is applied to a merging flow scenarios to reduce the risk of crowd disasters. Furthermore, to address the challenge of feature representation of complex human motion dynamics under the effect of HRI, an end-to-end robot motion planner based on deep neural network is proposed and trained using a deep reinforcement learning algorithm. The new approach avoids hand-crafted feature detection and extraction and thus improves the learning capability for complex dynamic problems.

Indoor Human Localization: A cooperative localization scheme using robot-smartphone collaboration (past project)

Overview: Smartphone-based human indoor localization was previously implemented using wireless sensor networks at the cost of sensing infrastructure deployment. Motivated by the increasing research attention on location-aware human-robot interaction, this project studies a robot-assisted human indoor localization scheme using acoustic ranging between a self-localized mobile robot and smartphones from human users. Data from the low-cost Kinect vision sensor are fused with smartphone-based acoustic ranging, and an extended Kalman filter based localization algorithm is developed for real-time dynamic position estimation and tracking. Real robot-smartphone experiments are performed, and performances are evaluated in various indoor environments under different environmental noises and with different human walking speed. Compared with existing indoor smartphone localization methods, the proposed system does not rely on wireless sensing infrastructure, and has comparable localization accuracy with increased flexibility and scalability due to the mobility of the robot.