Despite a surge in robotics research dedicated to inferring and understanding human intent, a universally accepted definition remains elusive since existing works often equate human intention with specific task-related goals. This article seeks to address this gap by examining the multifaceted nature of intention. Drawing on insights from psychology, it attempts to consolidate a definition of intention into a comprehensible framework for a broader audience. The article classifies different types of intention based on psychological and communication studies, offering guidance to researchers shifting from pure technical enhancements to a more human-centric perspective in robotics. It then demonstrates how various robotics studies can be aligned with these intention categories. Finally, through in-depth analyses of collaborative search and object transport use cases, the article underscores the significance of considering the diverse facets of human intention.
Figures & tables
Figure 1: Representation of the evolution of the brain potential measured before and after the volunteer performed an action. W marks the moment indicated by the volunteer as the one at which they decide to act. RP marks the moment at which the detected potential exceeds the average threshold that is considered an activation. Figure from Haggard (2005) .
Figure 2: Relationship between desires, beliefs and intention. The person’s beliefs (based on their knowledge, experience, memory, etc.) cause them to end up choosing a particular desire which is the one that can materialize an intention.
Figure 3: A model of the relationship between the intention and intentionality concepts. To have an intention is a pre-requisite to have an intentionality. Figure based on concepts presented in Malle and Knobe (1997) .
Criterion
Type of intention
Type of goal
Goal oriented – Implementation
Communication
Implicit – Explicit
Consciousness
Conscious – Unconscious
Number of agents
Individual – Collective
Temporality
Short-term – Long-term
Table 1: Summary of the different ways of classifying human intention according to the literature
Figure 4: Representation of the main relationships between intention categories.
Figure 5: Outline of the taxonomy presented for human intent.
Figure 6: Different perspectives considered about who is the agent detecting the intention.
Figure 7: Handover example. Using a trajectory predictor, the robot can predict how much the human is going to approach (implementation intention) and even the desired Object Transfer Point (goal-oriented intention) that the robot should reach.
Figure 8: Human-robot collaborative object transportation example. Human and robot transporting a bar. Force sensor attached to the robot’s wrist with which it can the human’s implicit intention and buttons on the handle of the object for explicit intention communication.
Figure 9: Human-robot pair navigating through a narrow door. Top left : Human-Robot pair navigating side-by-side until they reach a door. Top right : Human takes the lead but keeps taking into consideration the robot. Robot moves its left arm to allow the human to move forward. Bottom left : Human waits for the robot to finish passing through the door. Robot rolled up its right arm to reduce its footprint and moved its left arm to make the human to be in front of it. Bottom right : Human-Robot pair recover their side-by-side configuration.
Figure 10: Collaborative assembly example. Robot sees that the human is holding a screw and moving his arm towards a screwdriver. It predicts that in the human is going to take the screwdriver in the short term and use it to tighten the screw in the long term. Both predictions allows it to estimate how many time it has to take the next bar and when the shared space will be free.
Figure 11: Human-Robot collaborative search example. The human is holding an smartphone through which he can communicate explicitly with the robot in a bidirectional manner. The robot can also keep track of the human movement while it can see him.