PROGrasp: Pragmatic Human-Robot Communication for Object Grasping
This addresses the challenge of more intuitive human-robot interaction for object manipulation, though it is incremental as it builds on existing IOG systems by adding pragmatic inference.
The paper tackles the problem of enabling robots to grasp objects based on human intentions expressed through natural language, rather than explicit object categories, by introducing a new task called Pragmatic-IOG and a dataset IM-Dial, with their system PROGrasp showing effectiveness in both offline target discovery and online physical grasping experiments.
Interactive Object Grasping (IOG) is the task of identifying and grasping the desired object via human-robot natural language interaction. Current IOG systems assume that a human user initially specifies the target object's category (e.g., bottle). Inspired by pragmatics, where humans often convey their intentions by relying on context to achieve goals, we introduce a new IOG task, Pragmatic-IOG, and the corresponding dataset, Intention-oriented Multi-modal Dialogue (IM-Dial). In our proposed task scenario, an intention-oriented utterance (e.g., "I am thirsty") is initially given to the robot. The robot should then identify the target object by interacting with a human user. Based on the task setup, we propose a new robotic system that can interpret the user's intention and pick up the target object, Pragmatic Object Grasping (PROGrasp). PROGrasp performs Pragmatic-IOG by incorporating modules for visual grounding, question asking, object grasping, and most importantly, answer interpretation for pragmatic inference. Experimental results show that PROGrasp is effective in offline (i.e., target object discovery) and online (i.e., IOG with a physical robot arm) settings. Code and data are available at https://github.com/gicheonkang/prograsp.