Human instructors often refer to objects and actions involved in a task description using both linguistic and non-linguistic means of communication. Hence, for robots to engage in natural human-robot interactions, one needs to better understand the various relevant aspects of human multi-modal task descriptions. This paper analyses reference resolution to objects in a data collection comprising two object manipulation tasks (22 teacher student interactions in Task 1 and 16 in Task 2) and finds that 78.76% of all referring expressions to the objects relevant in Task 1 are verbally underspecified and 88.64% of all referring expressions are verbally underspecified in Task 2. The data strongly suggests that a language processing module for robots must be genuinely multi-modal, allowing for seamless integration of information transmitted in the verbal and the visual channel, whereby tracking the speaker’s eye gaze and gestures as well as object recognition are necessary preconditions.