  To perform household tasks, assistive robots receive commands in the form of
user language instructions for tool manipulation. The initial stage involves
selecting the intended tool (i.e., object grounding) and grasping it in a
task-oriented manner (i.e., task grounding). Nevertheless, prior researches on
visual-language grasping (VLG) focus on object grounding, while disregarding
the fine-grained impact of tasks on object grasping. Task-incompatible grasping
of a tool will inevitably limit the success of subsequent manipulation steps.
Motivated by this problem, this paper proposes GraspCLIP, which addresses the
challenge of task grounding in addition to object grounding to enable
task-oriented grasp prediction with visual-language inputs. Evaluation on a
custom dataset demonstrates that GraspCLIP achieves superior performance over
established baselines with object grounding only. The effectiveness of the
proposed method is further validated on an assistive robotic arm platform for
grasping previously unseen kitchen tools given the task specification. Our
presentation video is available at:
https://www.youtube.com/watch?v=e1wfYQPeAXU.
