Osiris⁺⁺: hierarchical representations for robotic-enabled precision agriculture

There has been significant development in agricultural robotics over the past few years in the pursuit of optimising efficiency and addressing issues such as labour shortages and humans performing hazardous and arduous tasks. Despite this, human-robot interaction in the agricultural sector remains largely unchanged, often requiring technical expertise, which hinders wide-scale adoption. This problem is particularly pronounced in the African context, where limited technical exposure and linguistic diversity pose significant barriers to the adoption of these technologies. While alternative means for human-robot collaboration have been developed, these methods are currently limited to indoor structured environments. In this work, we introduce Osiris++, a flexible approach designed to allow seamless communication between robots and humans on an array of precision agriculture tasks. We validate and evaluate the performance of Osiris++ in real-world agricultural environments, demonstrating that the system can create accurate and useful scene graphs that aid in solving the assigned tasks. This paves the way for the possibility of allowing natural language instructions, including those in African languages, to be issued to robots within the agricultural sector.