Despite encouraging progress in embodied learning over the past two decades, there is still a large gap between embodied agents' perception and human perception. Humans have remarkable capabilities combining all our multisensory inputs. To close the gap, embodied agents should also be enabled to see, hear, touch, and interact with their surroundings in order to select the appropriate actions. However, today's learning algorithms primarily operate on a single modality. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals jointly. The goal of this workshop is to share recent progress and discuss current challenges on embodied learning with multiple modalities.

The EML workshop will bring together researchers in different subareas of embodied multimodal learning including computer vision, robotics, machine learning, natural language processing, and cognitive science to examine the challenges and opportunities emerging from the design of embodied agents that unify their multisensory inputs. We will review the current state and identify the research infrastructure needed to enable a stronger collaboration between researchers working on different modalities.