Perception and Manipulation

Dual-arm Manipulation

Neural Motion/Task Planning

Robot Perception and Perceptual Learning

We are interested in attaining a 3D understanding of the world around us. In particular, the perception system provides important information that the robot has to use for interacting with users and environments.

Object Grasping and Object Manipulation

A service robot must be able to interact with the environment as well as human users. We are interested in fundamental research in object-agnostic grasping, affordance detection, task-informed grasping, and object manipulation.

Lifelong Interactive Robot Learning

No matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, the robot should be able to continually learn about new tasks from few training examples on-site by interacting with non-expert users.

Dynamic Robot Motion Planning

We are interested in attaining fully reactive manipulation functionalities in a closed-loop manner. Reactive systems have to continuously check if they are at risk of colliding while planners should check every configuration that the robot may attempt to use.

Exploiting Multimodality

A service robot may sense the world through different modalities that may provide visual, haptic or auditory cues about the environment. In this vein, we are interested in exploiting multimodality for learning better representations to improve robot's performance.

Dual-Arm Manipulation

A dual-arm robot has very good manipulability and maneuverability which is necessary to accomplish a set of everyday tasks (dishwashing, hammering). We are interested in efficient imitation learning, collabrative manipulation, and large object manipulation.