Gestures play a critical role in human-human and human-robot interaction. In task-based contexts, deictic gestures like pointing are particularly important for directing attention to task-relevant entities. While most work on task-based human-human and human-robot dialogue focuses on closed-world domains, recent research has begun to consider open-world tasks, where task-relevant objects may not be known to interactants a priori. In open-world tasks, we argue that a more nuanced consideration of gesture is necessary, as interactants may use gestures that bridge traditional gesture categories, in order to navigate the open-world dimensions of their task environment. In this work, we explore the types of gestures used in open-world task contexts, and their frequencies of use. Our results suggest a need to rethink the way that gesture analysis is approached in the study of human-human and human-robot interaction.
For task-based human-robot interaction (HRI), effective communication can greatly increase task effectiveness (Cantrell et al., 2011; Tellex et al., 2020). Critically, this includes both verbal and non-verbal communication (Mavridis, 2015). Effective non-verbal communication can include both gaze and gesture (De Angeli et al., 1998), and are fundamental in human-human communication and substantially contribute to fluent communication (Kendon, 1997; Kita, 2003; Goldin-Meadow, 1999; Ping and Goldin-Meadow, 2010). Accordingly, understanding and generating these non-verbal communication modalities is critical for effective human-robot communication.
Recently, work on natural language understanding in robotics (Han et al., 2022; Han and Williams, 2022; Culpepper et al., 2022; Williams and Scheutz, 2015b) has been expanding beyond traditional tabletop domains to consider open-world contexts. In contrast to a closed-world context where there is an assumption that all possible referents have an a-priori representation in the robot’s system, open-world contexts have referents which may be new or unknown to robot ahead of time. For example, if a nurse asks an assistive robot “Go to the kitchen to find a glass of water, then bring it to the patient”. When trying to figure out what “it” means in this sentence the robot needs to be aware of the glass of water. However if the robot is using a vision-based system to ground referring language it may only contain a representation of the objects currently visible in the room. In order to ground “it” to a particular object, the robot may need to create a new representation for the glass of water outside of the vision system. By allowing for new representations to be created outside of what the robot is already aware of (in this case through vision), it can then allow for understanding reference in an open-world context. Work in this area has led to a number of algorithms for open-world reference resolution (Williams and Scheutz, 2015b; Williams et al., 2016; Culpepper et al., 2022) to allow for this type of behavior.
While there is work on linguistic grounding in open-world contexts, work on robot gesture still largely assumes a closed world where gestural targets are visible, known, and close-by (Lücking et al., 2015; Sauppé and Mutlu, 2014). We argue that this has led to an overly narrow focus on understanding and generating specific, narrow classes of gestures in human-robot interaction; i.e., deictic gestures that focus an interactant’s attention on a visible, nearby area of the task environment. Based on this argument, we analyze the gestures used in a recent corpus of human-human interactions, collected in a novel task environment designed by Han et al. (2022) to elicit a more ecologically valid range of referring forms. As we will show, our analysis of the gestures used in this task context backs up our argument, yielding a novel taxonomy of gestures used in open-world dialogue contexts, and suggesting a need for human-robot interaction researchers to fundamentally rethink the types of gestures they are attempting to model in task-based human-robot interaction.
To understand the ways in which gesture in current task-based HRI are overly limited, let us first consider the linguistic work that specifically targets open-world interactions. While task-based natural language understanding and generation in human-robot interaction has traditionally considered only closed-world environments. There has recently been an increase in research relaxing this closed-world assumption to consider open worlds, especially in the context of reference resolution (Williams and Scheutz, 2015b; Williams et al., 2016; Culpepper et al., 2022).
Reference resolution is the process of identifying what knowledge associated with particular entities in a robot’s memory is being referred to by a speaker’s referring language. While traditional approaches to reference resolution (and the related process of language grounding) have only attempted to associate incoming referring expressions with pre-existing knowledge representations, work on open-world reference resolution has additionally modeled how listeners might assess when an incoming referring expression is likely to refer to a previously unknown entity, and how a new knowledge representation might be created in such cases through the process of hypothesization (Williams and Scheutz, 2015a,b). More recently, Culpepper et al. (2022) presented a novel algorithm that allows for incremental reference resolution, which allows for a real-time word-by-word processing within this type of open-world framework.
While there has been less work on open-world language generation, this too is starting to change. Han et al. (2022), for example, recently presented a novel experimental setup designed to capture natural references to both visible, previously visible, and not yet visible referents, in order to develop computational referring form selection models that can handle these sorts of open world references. Like prior work on open-world reference resolution, however, this work has been solely focused on natural language generation, and has not attempted to account for the role that nonverbal communication, especially gesture, plays in open-world dialogue.
Because gestures are known to be a fundamental part of human communication (Kita, 2003; Goldin-Meadow, 1999), the use of gesture to enhance human-robot interaction has attracted significant attention across the history of human-robot interaction (Waldherr et al., 2000). Effective gesture has been shown both to promote sociability and interactions, making robots more natural and enjoyable to work with (Kim et al., 2013; Salem et al., 2012); and to enhance the effectiveness and productivity of task-based interactions (Gleeson et al., 2013; Gross et al., 2017). Because of the important role gestures play in human-robot interaction, HRI researchers have devised a number of taxonomies for categorizing the different types of gesture that can be used – and understood – by robots. Many of the existing taxonomies used for gesture generation and understanding for HRI research primarily focuses on physical arm motions (Allwood et al., 2007; Dael et al., 2012). These taxonomies are often very complex: the BAP taxonomy (Dael et al., 2012), for example, has nearly 40 non-mutually exclusive codes for gesture, with complex and specific codes such as “Left arm action curved repetition” or “Asymmetrical arms action”. For understanding what physical movements are needed to create with robots these taxonomies can be very helpful, however these taxonomies are often not grounded with the intent of the gesture in mind. Additionally, research in the semiotics community (Goodwin, 2003) shows that physical manifestation is not always a good indicator of the intent of the gesture, as context and subtle indicators also play a large role in gestural meaning. This means that despite their comprehensive nature in terms of physical motion, these large-scale categorization systems fail to account for the ways that different types of gestures are typically used to achieve different types of communicative purposes in human-robot interactions.
In contrast, the taxonomy devised by McNeill and Levy (1982) for use in human-human interaction categorizes gestures according to a small number of conversational roles: deictic, iconic, metaphoric, beat, and emblematic. Deictic gestures, like pointing, direct an interactants’s visual attention to a particular object or location. Iconic gestures mime the physicality of an object or action to direct an interactant’s internal attention to a particular concept. Metaphoric gestures are used to convey more abstract concepts such as time. Beat gestures are used for pacing or timing of linguistic structure. Emblematic gestures (e.g. a wave or thumbs-up) have distinct meanings derived through social and cultural context. While, this taxonomy for categorizing gestures (McNeill and Levy, 1982) and their incorporation into language-based communication (McNeill, 1985) has been widely adopted across the fields of psycholinguistics and human-robot interaction (de Wit et al., 2022) it lacks the specificity, and grounding to physical movement for interpretability that is often required, especially when it comes to the wide variety of gestures that fit under the broader umbrella of “deictic gestures”.
Within the domain of human-robot interaction, there is wide recognition that understanding and generation of non-verbal communication is critical for situated interaction (Cantrell et al., 2011; Breazeal et al., 2005; Mavridis, 2015). While there has been work on all of the gestural categories described by McNeill and Levy (1982), deictic gesture in particular has attracted significant attention, due to its highly task-oriented and more easily interpretable nature. Indeed, in task-based human-robot interaction, there is often an exclusive focus on understanding and generating deictic gestures. In the foundational work of Sauppé and Mutlu (2014), a wide variety of subtypes of deictic gestures are studied, including pointing, presenting, touching, exhibiting, grouping, and sweeping, with each category defined according to the physical motion of the gesture. For example, a ‘touching’ gesture requires direct physical contact with the referent, while a ‘sweeping’ gesture used wide arm movements to direct attention to larger regions. Work on computational understanding and generation of deixis typically models deictic gestures by projecting a saliency cone from the origin of a deictic pointing gesture (Kranstedt et al., 2005) outwards in a particular direction. These cones can then be used in a multi-modal estimator that combines grounded language and non-verbal communication to identify which potential referent is most likely given the particular language and gestures used (Schauerte et al., 2010; Schauerte and Fink, 2010; Lücking et al., 2015).
Yet critically, these methods assume that the target of a deictic gesture is visible and known to the robot so it can find the most likely target that falls within the deictic cone. While this seems reasonable at first glance, it is clear that humans frequently use gestures to refer to objects that cannot be seen or may not be known to the other person they are gesturing to. A simple example of this is the way that iconic gestures help to draw users’ internal attention to a target referent representation, rather than drawing users’ visual attention to a target referent stimulus. Moreover, some researchers (Stogsdill et al., 2021; Enfield et al., 2007) have recently begun to explore types of gestures that arise in large-scale and open-world environments, which seem to further trouble the boundaries between these categories of gesture.
One example of how the boundaries between traditional gesture categories are being troubled and contested within psycholinguistics is the work by Enfield et al. (2007). They examined how general “pointing” gestures could have a more complex meaning than what is traditionally associated with deictic gestures. Specifically, Enfield et al. looked at how pointing was used during conversation in small Laotian villages and classified pointing gestures into two types; primary Big (B) and secondary Small (S) points. The B-points were composed of large arm movements, while S-points were smaller, single-armed gestures that had more complex hand movements. While B-points seemed to be used in the way typically expected of deictic gestures (i.e., to point to a physical location in space), S-points were instead found to have more complicated usage. Sometimes the S-points were used similarly to deictic gestures specifying a physical object or person which would be ambiguous through language alone. But other times, the S-Points were also used to refer to locations that were not in the current view while not necessarily pointing in the exact location of that object.
These S-points thus trouble McNeill and Levy (1982)’s conception of deictic gestures, in which the objective of a pointing gesture is to direct attention to a physical location via spatial information. The notion of “abstract deictic” gestures are explored in McNeill et al. (1993), which looks at how deictic gestures can be used to point to objects or people which are not physically there. While McNeill et al. (1993)’s “abstract deictic” gestures are used to point to entities without a physical presence, they still retain spatial information within a narrative context, being used to denote relative position in a non-present space. However, some of the behaviors present in Enfield et al. (2007)’s S-point’s includes pointing gestures which do not necessarily contain direct spatial information, but instead are used to create a gestural representation of an entity, present or not. This suggests there is non-spatial information that can also be conveyed by pointing. While this use of non-spatial pointing is explored within the semiotics community (Enfield et al., 2007; McNeill, 2003), these gestures are still often classified as “Deictic gestures”, despite their lack of spatial information making them inherently non-deictic. Perhaps this is due in part to the grounding of McNeill’s categories in studies of conversational rather than task-based dialog. We argue that because of the lack of spatial information, a non-spatial pointing gesture does not fit cleanly into McNeill’s “Deictic gesture” category. As such, this suggests a need to our work that studies task-based gestures in order to better understand the use of gestures in complex, large-scale, open-world environments.
These troubled category boundaries have also been recently noted in work in the HRI community. Specifically, Stogsdill et al. (2021) explored the use of vague, non-deictic pointing gestures that are very similar to the examples of S-Points found in Enfield et al. (2007). For example, if someone is trying to refer to the room next door, they may vaguely wave toward the room in question without pointing precisely or directing their gaze. Or, if someone is referring to another faraway city, they may point in a completely random direction. This use of an abstract pointing gesture removes the spatial information that is central to McNeill et al. (1993)’s deictic gestures. Instead, Stogsdill et al. (2021) argue that this gesture attempts to merely convey the concept of “away”, which may be closer to a metaphoric gesture than a deictic gesture.
This spectrum of category-spanning gestures remains understudied, yet critically points to a need to reconsider the categories of gesture used in task-based interaction. More specifically, in order to enable open-world Human-Robot Interaction, we argue that there is a fundamental need to question how and where existing approaches to gesture understanding and generation might fall short, and how these shortcomings might be grounded in the general taxonomies used by roboticists and psycholinguists to make sense of gesture.
In this work, we thus propose to investigate the following research questions:
R1: How might we better categorize the types of referring gestures used in open-world task-based environments?
R2: How prevalent are these different categories of gestures?
R3: Is the overwhelming focus of the HRI research community on precise, deictic, B-point gestures justified by this distribution of observed gestures?
To evaluate our research questions, we analyzed the experimental data from the experiments conducted by Han et al. (2022). We will briefly describe the context in which Han et al. collected that data, as reported in Han et al. (2022), to explain why this was an ideal dataset for answering our research questions. The task environment used in Han et al. (2022)’s work (shown in Figure 2) was partitioned into four quadrants, each containing a variety of colored blocks. In their experiment, pairs of participants (an instructor and a learner) participated in a sequence of four building tasks, one in each quadrant, in which the instructor taught the learner how to construct a different building from those blocks. Specifically, Han et al. (2022) designed their experiment so that each of these four building tasks required blocks not available within the task’s quadrant, including blocks seen in previous quadrants or located in quadrants that were to be visited in the future. This task structure was thus inherently open-world in nature; the instructor was required not only to refer to blocks that were immediately visible, but also to refer to blocks that had been seen in previous quadrants, as well as blocks whose locations were as-yet-unknown. By having discreet sections with items that participants were aware of and other sections they not aware of, they established quadrants that belonged to a current, closed working context and quadrants that belonged to an open-world. While Han et al. (2022) report designing this experiment to analyze open-world language production, we realized that their dataset could also be a rich source of open-world gesture production.
We analyzed twelve videos from Han et al. (2022)’s video dataset, a total of 337 minutes with an average of 28 minutes per video. From these videos, we identified 1067 gestures in total and an average of 89 gestures per video. In the next section, we will thus describe how we analyzed the gestures found in these videos.
To analyze those videos, we performed an iterative qualitative coding procedure. First, we began by breaking each video into a set of communicative action segments in which discrete gestures were used. Next, we performed open coding to identify, for each gesture, the (1) physical manifestation of the gesture and (2) information conveyed by the gesture. Then, we removed from consideration any gestures that did not appear to be related to the task itself, or which did not appear to provide the interactant with meaningful referential information to help them accomplish the task, such as beat gestures or emblematic gestures. Finally, we analyzed the literature on gesture classification to cluster gestures into discrete categories that were informed by prior literature whenever possible, while also striving to ensure (1) clear boundaries between gesture clusters, and (2) clear criteria for assignment of gestures to clusters.
Once a gesture taxonomy was identified, coders were used to identify when in the videos particular gestures were used. Then 17% of communicative action segments were used to establish an intercoder reliability rating (IRR) using Cohen’s Kappa Coefficient (Cohen, 1960) of κ = 0.35, denoting fair agreement. After an IRR was established, a single coder was used for the remaining segments. In the next section, we will describe the taxonomy of gestures that resulted from this qualitative coding procedure.
Based on our qualitative analysis, we formulated the following taxonomy of open-world task-based gestures which strike a balance between the intent of the gesture while allowing for the visually separable physical interpretability needed for robotics. This taxonomy is comprised of five key categories: (1) Precise Deictic Gestures, (2) Small Region Deictic Gestures, (3) Large Region Deictic Gestures, (4) Abstract Pointing Gestures, and (5) Iconic Reference Gestures. Summary statistics for the use of these gestures are shown in Table 1.
|Gesture Type||Count||pct of total|
|Small Region Deictic||434||40.6%|
|Large Region Deictic||13||1.2%|
We identified three distinct types of deictic gestures that manifest in open-world task-based interactions. The first category was what we term “Precise Deictic Gestures”. These gestures were those most closely related to the traditional conceptualization of Deictic Gesture, and were relatively common (36.6% of all analyzed gestures) due to their important role in specifying which block will be needed next for the experiment. The apparent purpose of this gesture was to physically direct attention to a single target object with a high level of specificity. This type of gesture physically manifested either as touching that object, or as pointing to that object in a way where it was the only task-relevant object entirely within the saliency cone extending from the gesturer’s hand (cf. Lücking et al., 2015; Schauerte et al., 2010). Thus, while the physical motions of precise deictic gestures are dependent on the environement, for example the gesturer may need to put their finger closer to an object if it it is near another object, the purpose of precise decictic gestures is maintained.
This category of gesture captures gestures that Sauppé and Mutlu (2014) would have categorized as exhibiting, touching, and presenting, as well as the single-target subset of those gestures Sauppé and Mutlu would have categorized as pointing. The difference in our categorization schemes is thus grounded in a difference in focus on motion vs purpose. That is, while Sauppé and Mutlu primarily focus on observable differences in how gestures are physically executed, we instead primarily focus on differences in what gestures are intended to achieve, while still taking into account the contextual information that the physical motion brings.
By analyzing these gestures in terms of purpose rather than motion alone, we can understand both when and why these gestures are used. In cases where the gesturer’s purpose is to direct attention to a single object, they generate a precise gesture in which only the target appears in the cone, because (cf. Schauerte and Fink (2010)) if the gesture were less precise, and multiple objects fell into the cone, then additional effort would be needed to further pick out the object through other channels such as language.
The second category of Deictic gesture we observed was what we term “Small Region Deictic Gestures” which accounted for a plurality (40.6%) of all gestures observed. The apparent purpose of this gesture was to direct attention toward a small group of objects, either due to an intention to pick out the blocks as a group, or due to an intention to pick out a single object within the group, without certainty as to which object should be attended to. This type of gesture physically manifested as pointing toward the general area containing those objects, so that all objects in the group were entirely within the saliency cone extending from the gesturer’s hand (cf. Lücking et al., 2015; Schauerte et al., 2010).
This category of gesture captures gestures that Sauppé and Mutlu (2014) would have categorized as grouping gestures, as well as the multi-target subset of those gestures Sauppé and Mutlu would have categorized as pointing. As above, then, the difference in our categorization schemes is grounded in a difference in focus on motion vs purpose.
The final category of obviously Deictic gesture we observed what we term “Large Region Deictic Gestures”. These gestures were very rare (1.2%), and were only used by some participants. Yet they were distinct enough in purpose and form to warrant separate consideration. The apparent purpose of this gesture was to direct attention to a large number of objects comprising multiple clusters, or to a large, general region of the task environment. This type of gesture physically manifested as a large, potentially full-arm gesture in the direction of the objects of interest, but without attempting to fit those objects into a saliency cone.
This category of gesture captures gestures that Sauppé and Mutlu (2014) would have categorized as grouping or sweeping gestures. Our categorization is more broad, however. Because we focus on on purpose rather than motion, we do not restrict this category to those that manifest as literal whole-arm sweeps, but rather include any gesture whose intent is to highlight a large region. For example, we include observed instances in which a speaker waves their hand across multiple clusters of blocks.
The next category of gesture we observed does not clearly fit into deictic gestures, nor does it clearly fit into another category, like metaphoric gesture. Rather, it represents a spectrum of gestures that fall somewhere between these categories. We term these gestures “Abstract Pointing Gestures”. While not as common (around 9.7% of observed gestures) as deictic gestures, these gestures were consistently used at least once by all participants whose data was analyzed.
The apparent purpose of this gesture was to indicate that a target referent was “elsewhere”, and possibly also to convey the direction in which the target referent was to be found. This gesture manifested in a variety of ways. In some cases, the gesture manifested as a point in the vague region of the target object, or in the direction of where the listener would need to go in order to begin traveling to the target. In either case, the gesture could be construed as casting an incredibly wide deictic cone; but we believe that the lack of precision in the gesture suggested that the speaker did not have a genuine expectation that the listener would follow their gaze, limiting the utility of modeling such a gesture as a cone.
Another reason for distinguishing this type of gesture from deictic gestures is other ways this could physically manifest that did not appear in this task-based dataset is due to the close distances between the speaker and all target referents, but which could manifest in other tasks with more varied out-of-context environments. A speaker trying to refer to something in another room, might wave generally or emblematically jerk their thumb over their shoulder. We would view these gestures as falling along the continuum of abstract gestures due to their shared intent.
This category of gesture captures those discussed by Stogsdill et al. (2021), McNeill et al. (1993)’s abstract deictic gestures, and some of the S-point gestures observed by Enfield et al. (2007). Our characterization of these gestures differs from that of Stogsdill et al. (2021), McNeill et al. (1993), and Enfield et al. (2007), however, in that we characterize them within a referential context, and ground them relative to other referential gestures within a comprehensive taxonomy. Additionally, they are grounded through our analysis of experimental data demonstrating how they are used in open-world task-based environments.
Thus far, we have been discussing gestures that are either clearly deictic, or that appear deictic in physical manifestation. We will now describe iconic gestures that we term “Iconic Reference Gestures”, which we observed to also play a key role in referential communication within the open-world task-based interactions that we analyzed. This category comprises a subset of the iconic gestures as delineated by McNeill and Levy (1982). However, we believe they are worth highlighting here as a separate category due to the referential purposes they achieve.
These gestures accounted for over one-tenth (11.8%) of the total gestures used. The apparent purpose of this gesture was to provide semantic content regarding the referent, to help disambiguate the semantic content of the speaker’s speech. As such, this type of gesture physically manifested as mimicry of the shape of a referenced block to make the shape of a block (such as a rectangle, or semi-circle), or tracing out the shape of the block in the air with an index finger.
These gestures play an especially important role in open-world communication. In fact, these gestures were the most common method we observed when speakers intended to refer to objects that were not currently visible. Despite the observed priamcy of these gestures for open-world task-based reference, these iconic gestures are not well studied in task-based HRI.
Our first research question was “How might we better categorize the types of referring gestures used in open-world task-based environments?” Our results show that a variety of different referring gestures are used in open-world task-based environments, including multiple types of deictic gestures, iconic gestures, and abstract gestures that trouble the previously delineated boundaries of traditional gesture categories.
As demonstrated above, our results suggest that deictic gestures may be best split into three categories, based on the intended specificity of the gesture. This suggests a need to shift from a focus on the physical form of different types of deictic gestures, to the way that speaker intent shapes gesture specificity.
Our results also highlight the need to consider iconic gestures when analyzing referring gestures. As demonstrated above, participants used a variety of iconic gestures to help communicate the properties of referents. We argue that these types of gestures are uniquely important to open-world task-based interactions, both to help describe the properties of previously-seen or as-yet-unseen objects – or to signal through the use of iconic gesture that those objects are not currently visible.
Finally, our results demonstrate the importance of Abstract Pointing Gestures; how these gestures are uniquely used in open-world task-based environments; and how imprecision and abstractness serve as tools to communicate this open-world status. These gestures, which do not fit cleanly into traditional gesture categories, demonstrate a need to think differently about gestures in open-world task-based environments, in a way that moves beyond traditional frameworks for categorizing gestures.
Our second research question was “How prevalent are these different categories of gestures?”, Table 1 shows the distribution of how the gestures observed in the analyzed dataset. While “Deictic Precise Gestures” are nearly the only type of referring gesture explored in the task-based human-robot interaction literature, they comprised only one-third of the gestures we observed (36.6%), making them only the second most common gesture type observed. In contrast “Deictic Small Region Gestures” were the most common gesture, constituting nearly half of observed gestures (40.6%), and one-fifth (21.5%) of gestures used were non-deictic gestures. This troubles the dominant perspective that nearly exclusively focuses on precise deictic gestures.
Our final research question was “Is the overwhelming focus of the HRI research community on precise, deictic, B-point gestures justified by this distribution of observed gestures”. Based on the types of gestures observed in the experiment, and their frequency of observation, we believe this overwhelming and myopic focus on precise deictic gestures is not justified. While the 37% frequency of use for “Precise Deictic Gestures” represents a meaningful quantity, it does not justify near-exclusive focus. These results suggest that the HRI research community should dramatically expand its scope of work to consider a wider variety of referring gestures.
Specifically, we recommend focusing on narrowing down the broader category of deictic gestures into different categories of deixes based on target specificity, and to explore the use of non-deictic gestures and the role they play in open-world referential communication. Similarly, while saliency cones are a good way to detect the target of a pointing gesture, as we start to incorporate non-pointing gestures, it is no longer sufficient. Understanding and generation of gestures which cannot be modeled by a saliency cone may be difficult, but it is also necessary for effective communication the open-world tasks that are prevalent within HRI. Overall, we suggest the HRI community should move away from the coarse, generic gesture categorization schemes they have relied on for so long.
Clear communication is critical for both human-human and human-robot task-based interaction; and clear communication in many task-based domains requires effective referential gestures. While current gesture research in task-based HRI is nearly exclusively focused on precise deictic gestures, as we transition to more realistic open-world tasks, we will need to move beyond these gestures.
Our results show that precise deicitic gestures only play a small part in task-based communication, suggesting that HRI research perhaps needs to be “pointed” in a new direction for both gesture generation and gesture understanding. We propose a new gesture taxonomy that can refocus the conversation about gesture to include gestures that are more suitable for open-world environments. We argue that this taxonomy is more effective at capturing the types of gestures used in tasks for both open-world and closed-world environments.
While our research shows the need to reevaluate gesture categorization, our results were limited due to the constraints of the experiment. One major limitations was the difficulty of seeing the direction of deictic gestures from the camera’s perspective, and thus, of differentiating “Precise Deictic Gestures”, “Small Region Deictic Gestures” and sometimes “Abstract Pointing Gestures”. Another major limitation was that certain gestures may have been under- or over-represented in the dataset we analyzed. Specifically, large deictic gestures may have been under-represented due to the task requiring only a single item at time, reducing the need for simultaneous reference for multiple blocks.
While this research itself may not have overt ethical implications, work on gesture understanding and generation does present ethical implications. Specifically, work on gesture in robotics brings along risks to privacy via perception, and risks of over-trust due enhanced anthropomorphic morphology. A major requirement for gesture generation and understanding is advanced capabilities in robotic perception. These perception methods bring along risks of data privacy and security as it can be unclear if and how perceptual data can be used during and after the robotic interaction. Additionally generation of gestures enhances the anthropomorphic morphology of the robot, which can lead to a higher and potentially false perception of the robot’s intelligence. This can lead to an overtrust in the robot’s intelligence and capabilities, and can lead to potentially dangerous outcomes (Robinette et al., 2016).
This work has been supported in part by the Office of Naval Research grant N00014-21-1-2418.
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