Friendly Virtual Agents

In this section, we use our Friendliness Model to generate FVAs. We describe the algorithm to generate movements corresponding to gaits, gestures, and gazing to make an agent appear friendlier in an AR environment.

6.5.1 Movement Characteristics Generation

We provide an overview of our end-to-end approach to simulating various movements of an FVA in Figure 6.4. At the start of the simulation, the environment state and a BFSM are initial- ized based on the user input and the intended tasks. We use a 3D human model for the represen-

Figure 6.4: FVA Generation: We highlight our approach to generating a FVA in an AR environ- ment. Our approach uses non-verbal movement characteristics of gaits, gestures, and gazing to convey friendliness. Based on the state-of-the-environment, these features are used to generate the movement of the FVA using a BFSM. We highlight the initialization steps on the left and various components of our runtime system on the right. The BFSM is used to compute each movement cue, which is finally used to update the various joint positions for each FVA. The FVA’s movements are visualized in an AR device (e.g., Microsoft HoloLens) using a 3D human model represented using a mesh and a hierarchical joint-based representation. We use 3D models corresponding to friends and celebrities to evaluate our approach.

tation of the FVA. This model is rigged using an automatic rigging software and a hierarchical skeleton is associated with appropriate joint values.

At runtime, we update the BFSM state according to the state of the environment and whether the FVA has completed the assigned tasks. Based on the updated state, a global planning step is used to compute the next intermediate goal position and configuration for the FVA. Next, we compute velocities for local navigation (if it needs to change location in the real world) and decide gaits, gestures, and gazing behaviors according to the BFSM state. We then update the joint positions and angles of the FVA’s hierarchical skeleton using the computed gaits, gestures,

and gazing features. We visualize the FVA using the selected 3D model in an AR device such as the Microsoft HoloLens. We describe these components in detail below.

6.5.2 Behavioral Finite State Machine

We use a BFSM to represent the behavioral state of the FVA and control its behavior. We assume that the environment consists of static obstacles and dynamic obstacles. We also assume that the locations of these static obstacles and their dimensions can be reliably estimated. Addi- tionally, dynamic obstacles can also be tracked, and their positions and velocities can be reliably determined at each time step. These dynamic obstacles may include humans in the real world (including the user) and virtual agents. During the initialization step, the environment’s state is initialized with positions and dimensions of the static obstacles and the current positions and velocities of the dynamic obstacles. We represent this state of the environment byE. Using the environment state and the intended task, a BFSM is initialized and the starting state of the BFSM is computed for a given task.

At runtime, we update the state of the BFSM based on the environment state and determine the FVA’s behavior within the context of the current task. The BFSM also computes a goal posi- tion~gi for each virtual agent. If there are obstacles or other virtual agents in the environment, we make sure that the resulting movements are collision-free.

6.5.3 Global and Local Navigation

If the goal position of virtual agents is different from their current position, then a navigation algorithm is used to compute the trajectory to the new position. We utilize the multi-agent sim- ulation framework,Menge(Curtis et al., 2016), to implement our local navigation algorithm. In this framework, a global navigation step breaks down the goal positions into intermediate goals that avoid collisions with the static obstacles in the environment. Moreover, a local navigation step provides collision-free navigation to the intermediate goals while avoiding collisions with dy- namic obstacles, which include other virtual agents and users’ avatars, using a reciprocal collision

avoidance approach. This computes a trajectory in the 2D plane. We ensure that no collisions are introduced due to other joint movements.

6.5.4 Gait Generation

When the agent is walking towards its goal position, as determined by the BFSM and the local navigation algorithm, its walking style is modeled using its gaitgdes. Based on the input friendliness valuefdes, we use the Friendliness Model to obtain the gaitgdes from the gait dataset Gas follows:

gdes =gj |j = n min

j=1 abs(fj −fdes) ∧ gj ∈G, (6.6)

wherenis the number of gaits in_Gandabs(·)represents the absolute value. This way we ensure that the gait of the FVA has the desired friendliness based on our model as presented in Section 4.1.

6.5.5 Gestures

We associate the BFSM state with gestures based on a mapping_GE :_I → _M, where_Mrep- resents a set of gesturesmj (j = 1...n)andIrepresents the set of BFSM states. This mapping is based on the BFSM context and on the use of open gestures to convey a high level of friendliness. For example, the FVA performs a waving gesture when the FVA exits the scenario or nods its head while indicating agreement with the user. Given the appropriate hand and/or head gestures mhand andmhead, we use the input friendliness valuefdesand the Friendliness Model formulation (Equations 6.3 and 6.4) to decide whether to use the open or closed gesture.

6.5.6 Gazing

In addition to friendliness, maintaining eye-contact or gazing also depends on the envi- ronment state (e.g., eye-contact should not be maintained while walking away (Narang et al.,

2016a)). Therefore, we associate a BFSM state to decide whether to maintain eye-contact or not GA:I→ξBF SM whereξBF SM is a boolean variable. We combine this function with the gazing behavior decided by the Friendliness Modelξf (Equation 6.5) and compute a variableξ:

ξ =ξf ∧ ξBF SM, (6.7)

whereξ = 0represents that the eye-contact is absent andξ = 1conveys that the eye-contact is being maintained.

We use the FVA’s 3D model to maintain eye-contact during a movement or task. Specifically, we manipulate the flexion and rotation angles of the FVA’s neck joint (Figure 6.5). We use the 3D positions of the user’s eye (represented by the rendering camera,~pc)) and the position of the FVA (~pF V A) to compute the flexionθf and rotationθrangles:

θf = asin ~p_c,z−~p_{F V A,z} ||~pc−~pF V A|| , (6.8) θr = asin ~p_c,x−~p_{F V A,x} ||~pc−~pF V A|| . (6.9)

We can also check the line-of-sight between the agent and the user using visibility queries. For this computation, we assume that the FVA is facing the x-axis and the z-axis points vertically up. Other orientations of the FVA can be similarly modeled by adding a coordinate transform in the computation.