5 accepted papers
Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversity and variance. We address this challenge by proposing a novel approach that leverages contrastive learning to extract a
In complex real-world tasks such as robotic manipulation and autonomous driving, collecting expert demonstrations is often more straightforward than specifying precise learning objectives and task descriptions. Learning from expert data can be achieved through behavioral cloning or by learning a rew…
Object manipulation performed by robots refers to the art of controlling the shape and location of an object through force constraints with robot end-effectors, both robot hands, and grippers. The success of task execution is usually guaranteed by the sense of touch. In this work, we present an opti
The sense of touch is essential for reliable mapping between the environment and a robot which interacts physically with objects. Presumably, an artificial tactile skin would facilitate safe interaction of the robots with the environment. In this work, we present our color-coded tactile sensor, inco…
Tactile sensing arrays are used to detect contacts of robotic systems with the environment. They are particularly useful for scenarios in which vision-based sensors cannot be used. Thanks to the presence of multiple sensing elements, tactile arrays also provide spatial information about the contact…