Top robotics research labs in the Bay Area and surrounding regions

Robotics research is at the forefront of technological advancement, shaping the innovations of today and laying the groundwork for tomorrow’s breakthroughs. In the vibrant landscape of the Bay Area and its neighboring regions in the US, a multitude of pioneering projects and research initiatives are underway.

This guide serves as a comprehensive tool, offering a glimpse into the diverse array of research labs across various universities in the region. Additionally, the guide highlights undergraduate and graduate-level robotics courses offered by each university, offering a glimpse into the educational pathways and learning opportunities available in this field.

1. San Francisco State University

Control for Assistive and Rehabilitation Robotics (CARE) Lab

The CARE Lab at San Francisco State University is dedicated to developing low-cost, wearable robotic devices for individuals with limited mobility. This research lab focuses on creating robotic prostheses to replicate the biomechanical movement of amputees’ missing limbs and robotic exoskeletons to assist individuals with paralysis. The lab’s innovative control strategies can determine human motion intent using a limited set of wearable sensors. Collaborative efforts span various engineering disciplines and medical fields, from initial design concepts to experimental practice with human subjects.

The CARE Lab prides itself on being an active research environment with over 15 diverse research students tackling various challenges in wearable robotics design and control. As a Hispanic-Serving Institution, SFSU supports over 50 undergraduate and graduate students in this robotics field, with 64% coming from underrepresented minority groups. Additionally, the lab is committed to modernizing the robotics and mechatronics curriculum to better prepare the workforce for Silicon Valley’s robotics industry.

Undergraduate Level Courses:

• ENGR 121 Gateway to Computer Engineering: Introduction to embedded computer systems with hands-on laboratory instrumentation, electronic circuit assembly, measurement, and testing.
• ENGR 415 Mechatronics: Multidisciplinary basics combining electronics, mechanical design, simulation, and control systems. Focus on system elements like sensors, actuators, and controllers.
• ENGR 447 Control Systems: Analysis and design of continuous and discrete control systems, including systems modeling, stability, compensation, and digital control methods.
• ENGR 470 Biomechanics: Mechanical behavior of biological tissues, emphasizing force analysis, stress analysis, and viscoelasticity.
• ENGR 478 Design with Microprocessors: Assembly language programming, system bus, interfacing, serial and parallel communications, and real-time embedded systems development.
• ENGR 498 Advanced Design with Microcontrollers: Advanced topics in microcontroller design, including architecture, interfacing, real-time operating systems, and system management.

Graduate Level Courses:

• ENGR 845 Neural-Machine Interfaces: Design and Applications: Concepts and challenges of neural-machine interfaces, combining neural signal processing, machine learning, and real-time system design for applications like neuroprosthetics.
• ENGR 868 Advanced Control Systems: Advanced feedback control techniques, sensor filtering, state space control, and real-time control implementation.
• ENGR 869 Robotics: Kinematics and kinetics of robotic manipulators, including serial and parallel manipulators and legged robots.
• ENGR 870 Robot Control: Control system design within robotics, covering feedback control, robot modeling, motion planning, and hands-on application in various practical robot areas.

2. University of the Pacific, Stockton

The School of Engineering and Computer Science at the University of the Pacific offers diverse robotics opportunities, spanning environmental monitoring, automated manufacturing, and biomedical devices. The research focuses on multi-agent systems for agriculture and ecological monitoring, as well as advanced biomedical devices. A dedicated robotics class is taught each semester, alternating between undergraduate and graduate levels, covering the full spectrum of robotics systems from mechanical and electrical components to programming.

Undergraduate Level Courses:

• MECH 104 Introduction to Mechatronics: Provides a broad understanding of mechatronic systems, covering computer-controlled machinery, sensing, actuation, and control. The course includes practical knowledge in developing simple embedded computer programs and the application of mechatronic systems in fields such as manufacturing, automotive systems, and robotics.
• BENG 124 Biomechanics: Discusses engineering mechanics concepts like stress, strain, deformation, and structural analysis with applications to biomechanical phenomena. Evaluates forces on human joints, forces in musculoskeletal tissue, and material properties of biological tissues.
• ECPE 155 Autonomous Robotics: Offers an overview of autonomous robotics design, including robot organization and control architectures, configurations of fixed and mobile robots, sensors, actuators, and the design of algorithms and knowledge representations.
• ECPE 161 Automatic Control Systems: Covers component and system transfer functions, open and closed loop responses, stability criteria, and applications to engineering systems, including a laboratory component.
• BENG 175 Human/Brain Machine Interface: Explores direct communication pathways between human signals and external devices, such as robotic arms or external keyboards. Topics include the physiology of signal generation, interface device design, and the development of computational algorithms for real-time control.

Graduate Level Courses:

• BENG 202 Biosensor: Introduces the basic features of biosensors, discussing common biological agents and their interfaces with transducers for biomedical applications. Focuses on optical biosensors and systems like fluorescence spectroscopy and microscopy.
• MECH 204 Advanced Mechatronics: Examines the design of integrated mechatronic systems, emphasizing mechanical, electrical, and control systems engineering. Laboratory work includes mechanism design, motors and sensors, interfacing and programming microprocessors, and mechanical prototyping.
• ECPE 255 Robotics: Explores high-level autonomous robotics, focusing on the theory, design, and implementation of intelligent and autonomous robots, including individual, swarm, and multi-agent robots. Combines theoretical learning with practical simulations and work on robot platforms.
• COMP 257 Advanced Algorithms: Covers the fundamentals of algorithm design, discussing basic paradigms for reasoning about algorithms, asymptotic complexity, and various techniques for designing efficient algorithms.

3. University of California, Berkeley

The BEST (Berkeley Energy and Sustainability Technologies/Expert Systems Technologies/Emergent Space Tensegrities) Lab

This BEST lab housed in 230 Hesse Hall on the UC Berkeley campus, spearheads interdisciplinary research at the forefront of design, computational design, sustainability, gender equity, and human-machine cognition. Under the direction of Professor Alice M. Agogino, the lab’s research spans various domains, including soft robotics, artificial intelligence, sensor fusion, and supervisory control. Professor Agogino, a distinguished member of the National Academy of Engineering, leads the lab’s efforts in advancing robotics and sustainable technologies.

One of the lab’s notable achievements is the development of sensor robots for space exploration, leading to the establishment of Squishy Robotics. Squishy Robotics, a spin-off from the BEST Lab’s research, focuses on utilizing sensor robots for environmental monitoring and hazard detection on Earth. Their HazMat robots provide critical data in hazardous environments, enhancing situational awareness without risking human safety. Collaborating with Professor Mark Mueller, the lab is also pioneering early wildfire and methane detection robotics.

High Performance Robotics (HiPeR) Lab

The High Performance Robotics Lab at UC Berkeley concentrates on fundamental robotics capabilities, particularly for Unmanned Aerial Systems (UAS). Led by Professor M.W. Mueller, the lab emphasizes safety, localization, and design enhancements through advanced algorithms, mechanical design, and control strategies. Notably, the lab emphasizes experimental validation of ideas, ensuring algorithms can be implemented and run in real-time on actual systems. Their research is often showcased through informative videos on their YouTube channel.

AUTOLab

Directed by Professor Ken Goldberg, UC Berkeley’s AUTOLab is a prominent research center focusing on robotics and automation sciences. With a team of 30 postdocs, PhD students, and undergraduates, the lab explores robust robot grasping and manipulation for various applications, including warehouses, homes, and robot-assisted surgery. Their research combines analytic theory with deep learning, emphasizing geometric and statistical models, motion planning, and control methods.

Undergraduate Level Courses at UC Berkeley:

• BIO ENG 102 Biomechanics: Analysis and Design: Introduces methods of continuum mechanics applied to biomechanical phenomena in biology and medicine.
• BIO ENG C119 Orthopedic Biomechanics: Covers statics, dynamics, and materials behavior in orthopedic biomechanics, including artificial joint design and analysis.
• MEC ENG 131/236C Vehicle Dynamics and Control: Explores automotive vehicle dynamics, safety systems, suspension analysis, and autonomous vehicle technology.
• MEC ENG 136/236 Introduction to Control of Unmanned Aerial Vehicles: Provides an overview of UAV control, including modeling, dynamics, and common control strategies.
• MEC ENG 139/239 Robotic Locomotion: Covers robotic locomotion principles, including kinematics, dynamics, control algorithms, and mechanical design.

Graduate Level Courses at UC Berkeley:

• EECS C106A/C206A: Introduction to robotics fundamentals, covering kinematics, dynamics, control, robotic vision, and computer vision.
• EECS C106B/EECS C206B Robotic Manipulation and Interaction: Advanced topics in robot control, manipulation, path planning, and active vision.
• MEC ENG C210 Advanced Orthopedic Biomechanics: Explores advanced topics in orthopedic biomechanics, including materials behavior and artificial joint design.
• MEC ENG 231A/B Experimental Advanced Control Design I/II: Hands-on experience in designing and analyzing control systems for industrial applications.
• MEC ENG 270 Advanced Augmentation of Human Dexterity: Focuses on designing assistive technologies for upper-limb mobility and dexterity.
• COMPSCI 287/287H Advanced Robotics/Algorithmic Human-Robot Interaction: Covers advanced topics in robotics, including planning, control, human-robot interaction, and algorithm design.

4. San Jose State University

Located within the Department of Mechanical Engineering at San Jose State University, the Biomechanics and Robotics (BioRob) Lab specializes in the development and application of biomechatronic robots, with a particular focus on maternal and child healthcare. Research efforts encompass wearable devices, surgical robots, and teleoperation systems for applications in physical therapy, virtual/augmented reality, and engineering education. The lab investigates various aspects of robotics, including smart materials, mechanical design, biofluid transport, tactile sensing, and haptic control, aiming to advance the integration of biomechatronic robotics in medical device development and therapy. For more information, visit the lab’s website at sites.google.com/sjsu.edu/biorob/home. Contact Dr. Lin Jiang at lin.jiang@sjsu.edu for inquiries.

Undergraduate Level Courses:

• ME 106 Fundamentals of Mechatronics Engineering: Covers foundational concepts in mechatronics, including analog and digital electronics, sensors, actuators, and microprocessor interfacing, with hands-on laboratory experience.
• TECH 115 Automation and Control: Explores the theory and application of automation elements, including sensors, controllers, actuators, and proportional, derivative, and integral control systems, with hands-on integration practices.
• CMPE 150 System Architecture and Electronic Design for Robotics: Focuses on design and architecture for autonomous and human-reliant robots, algorithm development for object perception, electronic design, and system integration, with emphasis on actuation, mobility, fault diagnosis, and self-calibration.
• CMPE 185 Autonomous Mobile Robots: Covers basic concepts and algorithms for autonomous mobile robots, including locomotion, kinematics, environment perception, probabilistic map-based localization and mapping, motion planning, and programming in Robot Operating System (ROS).
• ME 187 Automatic Control Systems Design: Analyzes dynamic systems in time and frequency domains, designs automatic control systems, and explores analog and digital control systems design using computer-aided tools.
• ME 192 Robotics and Manufacturing Systems: Explores scientific and engineering principles of robotics in mechanical manipulation, dynamics, sensing, actuation, control, computer vision, and manufacturing automation application.

Graduate Level Courses:

• ME 281 Advanced Control System Design: Establishes design criteria for digital control system design, including conventional and modern approaches, intelligent control system design, digital control system hardware and software, and microprocessor implementation of control systems.
• ME 284 Sensor Technology and Principles: Examines various sensor types and principles, sensor circuitry, signal characterization, processing, design, fabrication, and applications.
• ME 286 Autonomous and Connected Vehicles: Applies robotics, computer vision, and mechanical engineering fundamentals in a vehicle-to-vehicle and vehicle-to-infrastructure connected environment, focusing on autonomous driving and reducing traffic congestion.
• CMPE 249 Intelligent Autonomous Systems: Introduces autonomous systems and intelligent solutions for self-driving cars, covering multi-modal sensing, sensor fusion, AI computing, mapping, deep learning, object detection, perception, localization, prediction, path planning, control, reinforcement learning, and Robotic Operating System (ROS).

5. Santa Clara University

Robotic Systems Lab (RSL)

The Robotic Systems Laboratory (RSL) at Santa Clara University is a dynamic field robotics lab engaging approximately 100 students, both at the graduate and undergraduate levels. Specializing in the development and application of robotic systems and control technology across various domains including land, sea, air, and space, the lab’s projects serve the needs of external collaborators, partners, and sponsors. Notable projects include advanced marine robots for government agencies, autonomous rovers for agricultural tasks, drones for observational and mapping purposes, and satellite mission control technology for operating NASA and industry spacecraft.

Under the mentorship of faculty and staff, students conduct research in multi-robot control techniques, advanced anomaly management, and human-robot collaborative manipulation. One of the distinctive aspects of the program is its emphasis on hands-on operational experiences, where students learn to deploy and operate real-world robotic systems to provide services to industry and government partners. For example, students have been involved in conducting satellite mission control services for NASA and industry partners for over 15 years, using student-developed robots to map and explore Lake Tahoe with agencies like the US Geological Survey.

Undergraduate Level Courses:

• MECH 143: Mechatronics: Introduces students to electromechanical components and systems, covering electronic components/circuitry, mechanism configurations, and programming constructs.
• ENGR 20: Topics in Robotics: Explores sensing, actuation, and control techniques and components in the development of robotic systems or subsystems.
• ENGR 180 Marine Operations / ENGR 181 Advanced Marine Operations: Provides hands-on experience in designing and operating marine robots, including ocean deployments of tethered underwater robots.
• ELEN 131: Introduction to Robotics: Offers an overview of robotics, covering control, artificial intelligence, computer vision, kinematics, dynamics of robot manipulators, servo-control design, trajectory planning, obstacle avoidance, sensing, vision, and task planning.

Graduate Level Courses:

• ELEN 337/MECH 337 Robotics I: Covers robotics fundamentals including control, AI, computer vision, kinematics, dynamics of robot arms, task planning, and trajectory planning.
• ELEN 338 Robotics II/ MECH 338 Robotics II: Focuses on joint-based control, linear control of manipulators, and PID control.
• ELEN 339 Robotics III/ MECH 339: Explores intelligent control of robots, incorporating neural networks, fuzzy logic, and selected topics in current research.
• MECH 207, 208, 209 / ELEN 207, 208, 209 Advanced Mechatronics I, II, III: In-depth study of mechatronic systems design, implementation, and control, including sensors, actuators, controllers, and hardware/software implementations.
• ELEN 331 Autonomous Driving Systems (and Lab): Provides hands-on experience in modules of autonomous driving systems including localization, sensor fusion, perception, path planning, control, and decision-making.
• MECH 292 Special Topics in Mechatronic Systems Engineering: Offers various special topics courses such as marine systems, UAV design, multi-robot systems, and robotic vision systems.
• MECH 311 Modeling and Control of Telerobotic Systems: Explores case studies of telerobotic devices and mission control architectures, focusing on analysis and control techniques relevant to remote operation.

6. Stanford University

Assistive Robotics and Manipulation Lab (ARMLab)

The Assistive Robotics and Manipulation Lab (ARMLab) at Stanford University comprises approximately 10 graduate and undergraduate students dedicated to advancing collaborative robotic applications. The lab’s research focuses on enabling robots to work closely alongside humans, enhancing dexterity, manipulation capabilities, and learning efficiency. Projects include robotic finger design and modeling to improve humanoid robot manipulation, and leveraging mixed reality to enhance human-robot communication for task learning. Additionally, the lab explores powered upper-limb prostheses to better estimate and act on human wearer intention, thus augmenting agency and manipulation capability. A common thread in the lab’s research is the development of robots capable of effectively collaborating with humans on complex tasks, necessitating advanced modeling techniques of both tasks and humans.

Biomechatronics Lab

Stanford University’s Biomechatronics Lab focuses on developing wearable robots to enhance efficiency, speed, and balance during walking and running, particularly for individuals with disabilities. Research efforts aim to expedite the design process through the development of versatile prostheses and exoskeleton emulators, along with algorithms for human-in-the-loop optimization. The lab conducts basic scientific research on topics such as ankle push-off’s role in balance and the impact of arm swinging on energy economy. Additionally, the lab designs efficient autonomous devices like walking robots and electroadhesive clutches, collaborating with spin-out companies to translate research outcomes into products.

Collaborative Haptics and Robotics in Medicine (CHARM) Lab

Led by Prof. Allison Okamura, the Collaborative Haptics and Robotics in Medicine (CHARM) Lab at Stanford University focuses on haptics, medical and rehabilitative robotics, and soft robotics. With approximately 20 PhD students and postdocs, the lab designs and studies haptic and robotic systems using analytical and experimental approaches. Research spans the development of haptic devices for education, teleoperation, and navigation, as well as low-cost devices for stroke rehabilitation and enhancing minimally invasive surgery. In the realm of soft robotics, the lab designs various actuators and systems, including innovative robots like the ‘vine’ robot capable of navigating environments through growth.

Undergraduate Level Courses:

• CS225A Experimental Robotics: Hands-on laboratory experience in robotic manipulation covering topics such as kinematics, dynamics, control, compliance, and human-robot interfaces.
• AA277 Multi-Robot Control and Distributed Optimization: Survey of current research topics in multi-robot systems including consensus, formation control, and sensor deployment.
• CS223A Introduction to Robotics: Foundations in modeling, design, planning, and control, covering geometry, kinematics, dynamics, motion planning, and control.
• CS237A/AA174A Principles of Robot Autonomy: Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities.
• CS274B/AA174B Principles of Robot Autonomy II: Advanced principles for enabling robots to autonomously learn new skills and interact with the environment.

Graduate Level Courses:

• ME326 Collaborative Robotics: Focuses on effective collaboration between robots and other agents, covering task objectives, perception and control, teammate behavioral modeling, and inter-agent communication.
• CS327A Advanced Robotic Manipulation: Advanced control methodologies and design techniques for complex robotic systems, including operational space dynamics and human motion synthesis.
• CS336 Robot Perception and Decision-Making: Studies decision-making in robots based on partial, noisy sensory data.
• CS326 Topics in Advanced Robotic Manipulation: Surveys concepts in autonomous robotic manipulation, including motion planning, control, and machine learning approaches.

7. University of California, Davis

Dr. Stavros G. Vougioukas Research Lab

Dr. Stavros G. Vougioukas Research Lab at the University of California, Davis focuses on mechanization, agricultural robotics, and automation for specialty crop production. The lab aims to enhance efficiency and working conditions for human workers or replace labor in certain tasks through the development of methodologies and technologies. Key research areas include:

• Robotic Harvesting: Investigating the use of robotic harvesters with multiple arms to increase harvesting speed. Research explores alternative mechanical designs and efficient distribution of work among arms to achieve high picking speeds.
• Model-Based Design Tools: Developing model-based design tools to examine interrelationships among orchard layout, tree canopy geometry, spatial fruit distribution, harvester design, and worker activities. These tools accelerate the development of next-generation orchard mechanization and automation systems.
• Human-Robot Collaboration and Multi-Robot Coordination: Exploring ways to enhance labor efficiency and human factors by designing advanced autonomous machines that collaborate with agricultural workers. Challenges include developing mechanistic models of agricultural worker activities for safe and efficient collaboration and coordinating autonomous agricultural vehicles to improve field and orchard logistics while ensuring safety.

Undergraduate Level Courses:

• EAE 130A Aircraft Performance & Design: Provides a major aircraft design experience with constraints including aerodynamics, performance analysis, weight estimation, stability and control, and engineering standards.
• EAE 143A Space Vehicle Design: Covers governing equations and operational practices of robotic and human space travel, introducing principles of Systems Engineering through spacecraft reverse-engineering and design projects.
• EEC 189H/289H Special Topics in Electrical Engineering & Computer Science: Robotics: Explores special topics in Robotics.
• EEC 195A Autonomous Vehicle Design Project: Involves designing and constructing an autonomous race car, including speed control circuits, track sensing circuits, and steering control loop testing in groups.

Graduate Level Courses:

• MAE 225 Spatial Kinematics & Robotics: Covers spatial kinematics, screw theory, spatial mechanisms analysis and synthesis, robot kinematics and dynamics, workspace analysis, path planning, programming, and real-time software implementation.
• MAE 252 Information Processing for Autonomous Robotics: Explores computational principles for sensing, reasoning, and navigation for autonomous robots.
• EEC 255 Robotic Systems: Introduces robotic systems, covering mechanical manipulators, kinematics, positioning, path planning, dynamics, motion programming, and control algorithm design.
• MAE 258 Hybrid Electric Vehicle System Theory & Design: Focuses on advanced vehicle design for fuel economy, performance, and low emissions, considering regulations, societal demands, and manufacturability, along with analysis and verification of computer design and control of vehicle systems.
• MAE 275 Guidance & Control of Unmanned Aerial Systems: Introduces challenges in guiding and controlling unmanned aerial systems, covering coordinate frames, kinematics, dynamics, autopilot design, sensor models, state estimation, guidance design, and path planning.
• EEC 295 Systems, Control & Robotics Seminar: Offers seminars on current research in systems and control, featuring presentations and lectures on robotics research and technology by faculty and visiting experts.

8. University of California, Merced

Dr. Ricardo de Castro’s Research Lab

Dr. Ricardo de Castro’s Research Lab at the University of California, Merced focuses on electric and robotic vehicles, aiming to achieve high energy efficiency, durability, and reliability of energy storage systems through power conversion and advanced control methods. The lab comprises 3 graduate and 3 undergraduate students, along with 1 visiting PhD student.

Key research areas include:

• Battery Balancing Systems: Investigating hybrid battery balancing systems capable of simultaneously equalizing battery capacity and temperature while enabling hybridization with additional storage systems like supercapacitors. The research integrates these functions into a single system, reducing the cost of power conversion in hybrid energy storage units.
• Electric Mobility and Powertrain Optimization: Studying electric vehicle powertrains and propulsion systems, leveraging compact in-wheel electric motors for fast, accurate, and energy-efficient control of torque applied to the wheels. Research focuses on enhancing control of automated vehicles and estimation of road surface grip, while incorporating user preferences in maneuver design and execution.

Undergraduate Level Courses:

• COGS 125 Introduction to Artificial Intelligence: Provides an overview of intelligent systems construction and analysis, covering agent architectures, problem-solving, heuristic search, knowledge representation, reasoning, planning, communication, perception, robotics, and machine learning.
• COGS 128 Cognitive Engineering: Introduces cognitive engineering with a focus on cognitive science, including human-computer interaction, human-robot interaction, speech recognition systems, animated characters, and virtual reality systems.
• CSE 171 Game Programming: Covers algorithms and techniques for interactive 3D graphics implementation in computer games, robotics simulators, and virtual reality.
• AE 173 Design of Unmanned Aerial Vehicles: Covers conceptual and preliminary design of autonomous aerial vehicles (UAVs), including aerodynamics, vehicle dynamics, control system design, and flight path planning and optimization.
• CSE180 Introduction to Robotics: Covers basics of robotics focusing on algorithmic aspects such as spatial modeling, planning, and sensor processing, with a hands-on component.

Graduate Level Courses:

• EECS 270 Robot Algorithms: In-depth study of algorithmic techniques to solve fundamental robotic problems, emphasizing probabilistic aspects, sensor fusion, and mission planning.
• EECS 281 Advanced Topics in Robotics: Explores contemporary issues in mobile robotics including cooperative robotics, manipulation, humanoid robotics, human-robot interfaces, and robot hardware.
• EECS 283 Advanced Topics in Internet of Things and Sensing Systems: Reviews current results in intelligent systems, covering areas like artificial intelligence, machine learning, networking, robotics, security, and signal processing.
• EECS 285 Advanced Topics in Motion Planning: Covers advanced algorithms in motion planning research domain and their applications in robotics, computer animation, cognitive science, and bioinformatics.
• ME 290 Robotic Vehicles: Provides foundations for designing, implementing, and testing robotic vehicles, covering modeling, motion planning, control, actuation, sensing, cooperative motion control, formation, locomotion, and learning-based control algorithms.

9. University of California, Santa Cruz

Hybrid Systems Laboratory (HSL)

The Hybrid Systems Laboratory (HSL) at the University of California, Santa Cruz, within the Department of Electrical and Computer Engineering, specializes in analyzing hybrid dynamical systems and designing hybrid feedback control algorithms. Research at HSL focuses on theoretical approaches with numerical validation through simulations, occasionally supplemented by experimental validations. Key areas of emphasis include nonlinear hybrid dynamics, cyber-physical systems, and feedback systems applicable to robotics, aerospace, power systems, and biology.

Autonomous Systems Lab

The Autonomous Systems Lab (ASL) at the University of California, Santa Cruz, under the direction of Professor Gabriel Elkaim, specializes in guidance, navigation, and control, path-planning, computer vision, and sensor fusion within the realm of autonomous systems. The lab’s mission is to reduce the cost of autonomous systems through open-source development of complete systems and onboard sensors, aiming to enhance robotics and autonomous systems’ accessibility by maximizing processing power while minimizing sensor costs.

Undergraduate Level Courses:

• ECE 8 Robot Automation: Intelligence through Feedback Control: Introduces dynamical systems, feedback control, and robotics, covering fundamental concepts and design of feedback-control laws.
• ECE 10 Fundamentals of Robot Kinematics and Dynamics: Explores mathematical models to analyze robot kinematics and dynamics, including vector algebra, differential equations, and computer simulations.
• ECE 118/218 Introduction to Mechatronics: Covers technologies and integration techniques for mechatronic systems, including electronics, software design, motors, sensing, and mechanical design.
• ECE 121 Introduction to Microcontrollers: Focuses on microcontroller-based embedded systems design, covering low-level functionality, peripheral manipulation, communications protocols, and device driver programming.
• ECE 141 Feedback Control Systems: Studies analysis and design of continuous linear feedback control systems using principles like root locus, frequency response, and state space methods.
• ECE 163/263 Small Scale UAV Modeling and Control: Explores modeling, simulation, and control of small-scale unmanned aerial vehicles, emphasizing control applications from flight stabilization to path planning.
• ECE 167 Sensors and Sensor Systems: Introduces fundamental issues in sensing and sensor integration into digital systems, covering topics like temperature, motion, light, and noise.
• ECE 176 Energy Conservation and Control: Examines AC/DC electric-machine drives for speed/position control and their applications in electric transportation, robotics, and energy conservation.

Graduate Level Courses:

• ECE 215 Models of Robotic Manipulation: Focuses on mathematical models for analyzing, designing, and programming robotic manipulators, including forward and inverse kinematics, Jacobian matrix, trajectory generation, and dynamic models.
• ECE 216 Bio-Inspired Locomotion: Presents principles of biological locomotion and applies them to robotics design problems.
• CMPM 237 Advanced Topics in Human-Robot Interaction: Explores current topics in human-robot interaction design and research, covering telepresence, collaborative robotics, and social robotics.
• ECE 240 Linear Dynamical Systems: Introduces applied linear algebra and linear dynamical systems with applications in circuits, signal processing, communications, and control systems.
• ECE 241 Introduction to Control Systems: Provides a graduate-level introduction to control of continuous linear systems using classical feedback techniques, including root locus and frequency response design.
• ECE 242 Applied Feedback Control Systems: Explores state space control, discrete time control, and case studies in control design, with a focus on designing and implementing feedback controllers.
• ECE 246 Hybrid Dynamical Systems: Examines the modeling and analysis of hybrid dynamical systems, covering stability, convergence, robustness, and simulation techniques.