We present a retrospective on the state of Embodied AI research. Our analysis focuses on
13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are …

The development of embodied agents that can communicate with humans in natural
language has gained increasing interest over the last years, as it facilitates the diffusion of …

Autonomous exploration has many important applications. However, classic information
gain-based or frontier-based exploration only relies on the robot current state to determine …

Robots require a semantic understanding of their surroundings to operate in an efficient and
explainable way in human environments. In the literature, there has been an extensive focus …

Reinforcement learning (RL) has achieved promising results in solving numerous
challenging sequential decision problems. To address the issue of sparse extrinsic rewards …

The problem of prostate segmentation from Magnetic Resonance Imaging (MRI) is an
intense research area, due to the increased use of MRI in the diagnosis and treatment …

Embodied AI is a recent research area that aims at creating intelligent agents that can move
and operate inside an environment. Existing approaches in this field demand the agents to …

Most existing reinforcement learning (RL) algorithms are solely applied to scenarios with
pure discrete action space or pure continuous action space. However, in certain real-world …

In realistic sparse reward tasks, existing theoretical methods cannot be effectively applied
due to the low sampling probability ofrewarded episodes. Profound research on methods …

Exploration systems are critical for enhancing the autonomy of robots. Due to the
unpredictability of the future planning space, existing methods either adopt an inefficient …