Task Planning with a Weighted Functional Object-Oriented Network
This addresses the challenge of balancing workload in human-robot collaboration, particularly for risky or complex manipulation tasks like cooking, though it is incremental as it builds on an existing FOON framework.
The paper tackles the problem of collaborative task planning between robots and humans by proposing a weighted functional object-oriented network (FOON) and a task planning algorithm to allocate actions based on success probabilities, resulting in higher success rates for complex tasks compared to robots working alone.
In reality, there is still much to be done for robots to be able to perform manipulation actions with full autonomy. Complicated manipulation tasks, such as cooking, may still require a person to perform some actions that are very risky for a robot to perform. On the other hand, some other actions may be very risky for a human with physical disabilities to perform. Therefore, it is necessary to balance the workload of a robot and a human based on their limitations while minimizing the effort needed from a human in a collaborative robot (cobot) set-up. This paper proposes a new version of our functional object-oriented network (FOON) that integrates weights in its functional units to reflect a robot's chance of successfully executing an action of that functional unit. The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort. Through a number of experiments, this paper shows several successful cases in which using the proposed weighted FOON and the task planning algorithm allow a robot and a human to successfully complete complicated tasks together with higher success rates than a robot doing them alone.