Embodied systems are playing an increasingly important role in our lives. Examples include, but are not limited to, autonomous driving, drone delivery, and service robots. In real-world deployments, the systems are required to safely learn and operate under the various sources of uncertainties. As noted in the “Roadmap for US Robotics (2020)”, safe learning and adaptation is a key aspect of next-generation robotics. Learning is ingrained in all components of the robotics software stack including perception, planning, and control. While the safety and robustness of these components have been identified as critical aspects for real-world deployments, open issues and challenges are often discussed separately in the respective communities. In this workshop, we aim to bring together researchers from machine learning, computer vision, robotics, and control to facilitate interdisciplinary discussions on the topic of deployable decision making in embodied systems. Our workshop will focus on two discussion themes: (i) safe learning and decision making in uncertain and unstructured environments and (ii) efficient transfer learning for deployable embodied systems. To facilitate discussions and solicit participation from a broad audience, we plan to have a set of interactive lecture-style presentations, focused discussion panels, and a poster session with contributed paper presentations. By bringing researchers and industry professionals together in our workshop and having detailed pre- and post-workshop plans, we envision this workshop to be an effort towards a long-term, interdisciplinary exchange on this topic.