Robotics, Computer Vision, and Graphics

Robotics, Computer Vision, and Graphics at UMass Amherst represent the interface between computers and the world in which we live. To interact naturally with computers, we must have computers that can relate to their environment through visual and physical interactions, from recognizing the face of an approaching person to learning about dynamics by bouncing a ball. In robotics, our expertise ranges from sophisticated grasping techniques and novel motion planning methods to complex tool use and experimenting with new types of dynamically stable robots. In computer vision, our strengths include scene modeling, face identification, object recognition, and reading the text of signs in complex outdoor environments. A major cross-cutting interest is the desire to model basic learning processes in humans and machines using data acquired from sensors mounted on robots and mobile video cameras. By adapting our computers' strategies of grasping, reaching, moving, and recognizing to real-world data rather than synthetic laboratory data, we are building systems robust enough to operate in realistic scenarios.

Related faculty

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Vangelis Kalogerakis

Computer graphics and vision, geometric modeling, 3D deep learning, animation, shape analysis and synthesis, scene modeling, 3D reconstruction.

Donghyun Kim

Robotics, legged locomotion, dynamics and control, vision sensing, machine learning.

Erik Learned-Miller

Computer vision and machine learning; deep learning; probabilistic and statistical methods in vision and image processing. 

Scott Niekum

Imitation learning, reinforcement learning, AI safety, robotic manipulation, and human-robot interaction.

Grant Van Horn

Grant's research lies at the intersection of computer vision and machine learning, focusing on creating intelligent information systems that blend human experti

Rui Wang

Computer graphics, global illumination algorithms, real-time rendering, graphics hardware, 3D scanning and image processing.

Hao Zhang

Robotics, lifelong autonomy, human-robot teaming, and robot learning and adaptation.

Related centers & labs

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Autonomous Learning Laboratory

Focuses on both machine and biological learning including reinforcement learning, safe machine learning, and biologically inspired machine learning.

Computer Vision Research Lab

Investigating the scientific principles underlying the construction of integrated vision systems and the application of vision to real-world problems. 

Human-Centered Robotics Lab

Dedicated to cutting-edge research on lifelong collaborative autonomy, with the goal of enabling robots to operate and adapt over long periods of time.

UMass Graphics Lab

Focuses on developing new algorithms and techniques in computer graphics and visual computing.