Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
This addresses the challenge of human-robot interaction for manipulation tasks, but it is incremental as it builds on existing methods for language grounding.
The paper tackles the problem of grounding natural language instructions for robotic manipulation by separately learning world representation and language grounding to address data scarcity, and proposes Bayesian learning to resolve inconsistencies using implicit spatio-relational information, demonstrating feasibility with a physical robotic arm.
Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.