We present a systematic study on a new task called dichotomous image segmentation (DIS),
which aims to segment highly accurate objects from natural images. To this end, we …

Self-occlusion is challenging for cloth manipulation, as it makes it difficult to estimate the full
state of the cloth. Ideally, a robot trying to unfold a crumpled or folded cloth should be able to …

Robotic Grasping has always been an active topic in robotics since grasping is one of the
fundamental but most challenging skills of robots. It demands the coordination of robotic …

Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually
have a double-loop training process, alternating between learning a reward function and a …

Adaptable models could greatly benefit robotic agents operating in the real world, allowing
them to deal with novel and varying conditions. While approaches such as Bayesian …

Manipulating deformable objects, such as ropes and clothing, is a long-standing challenge
in robotics, because of their large degrees of freedom, complex non-linear dynamics, and …

Manipulation of granular materials such as sand or rice remains an unsolved problem due to
challenges such as the difficulty of defining their configuration or modeling the materials and …

Dichotomous image segmentation (DIS) with rich fine-grained details within a single image
is a challenging task. Despite the plausible results achieved by deep learning-based …

Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually
have a double-loop training process, alternating between learning a reward function and a …

Deformable object manipulation is a long-standing challenge in robotics. While existing
approaches often focus narrowly on a specific type of object, we seek a general-purpose …