Imitation and Social Learning in Robots, Humans and Animals: Task learning through imitation and human–robot interaction

behaviors embed representations of goals in the form of abstracted environmental states. This is a key feature critical for learning from experience. To learn a task, the robot must create a mapping between its perception (observations) and its own behaviors that achieve the observed effects. This process is enabled by abstract behaviors, the perceptual component of a behavior, which activate each time the robot’s observations match the goal(s) of a primitive behavior. This correlation enables the robot to identify its own behaviors that are relevant for the task being learned. Primitive behaviors execute the robot’s actions and achieve its goals. They are also used for communication and interaction. Acting in the environment is a form of implicit communication. By using evocative actions, people and other animals convey emotions, desires, interests, and intentions. Action-based communication has the advantage that it need not be restricted to robots or agents with a humanoid body or face: structural similarities between interacting agents are not required for successful interaction. Even if there is no direct mapping between the physical characteristics of the robot and its user, the robot can still use communication 1.4 Communication by Acting a Means for Robot-Human Interaction 5 through action to convey certain types of messages, drawing on human common sense [16]. 1.4 Communication by Acting a Means for Robot-Human Interaction Consider a prelinguistic child who wants an out-of-reach toy. The child will try to bring a grown-up to the toy and will then point and reach, indicating his desires. Similarly, a dog will run back and forth to induce its owner to come to a place where it has found something it desires. The ability of the child and the dog to demonstrate their desires and intentions by calling a helper and mock-executing actions is an expressive and natural way to communicate a problem and need for help. The human capacity to understand these intentions is also natural and inherent. We apply the same strategy in enabling robots to communicate their desires and intentions to people. The action-based communication approach we propose is general and can be applied on a variety tasks and physical bodies/platforms. The robot performs its task independently, but if it fails in a cognizant fashion, it searches for a human and attempts to induce him to follow it to the place where the failure occurred, and then demonstrates its intentions in hopes of obtaining help. Attracting a human to help is achieved through movement, using back-and-forth, cyclic actions. After capturing the human’s attention, the robot leads the human helper to the site of the task and attempts to resume its work from the point where it failed. To communicate the nature of the problem, the robot repeatedly tries to execute the failed behavior in front of its helper. This is a general strategy that can be employed for a wide variety of failures but, notably, not for all. Executing the previously failed behavior will likely fail again, effectively expressing the robot’s problem to the human observer. 1.4. 1 Experiments in Communication by Acting We implemented and tested our concepts on a Pioneer 2-DX mobile robot, equipped with two sonar rings (8 front and 8 rear), a SICK laser range-finder, a pan-tilt-zoom color camera, a gripper, and on-board computation on a PC104 stack. The robot had a behavior set that allowed it to track cylindrical colored targets (Track (ColorOfTarget, GoalAngle, GoalDistance)), to pick up PickUp(ColorOfObject), and to drop small colored objects Drop. These behaviors were implemented in AYLLU [17]. In the validation experiments, we asked a person that had not worked with the robot before to be near-by during task execution and to expect to be engaged in an interaction. There is no initial assumption that people will be helpful or motivated to assist the robot. The robot is able to deal with unhelpful or misleading humans 6 Task Learning Through Imitation and Human-Robot Interaction by monitoring their presence along with its progress in the task. The following main categories of interactions emerged from the experiments: uninterested: the human was not interested, did not react to, or did not understand the robot’s need for help. As a result, the robot searched for another helper. interested but unhelpful: the human was interested and followed the robot for a while, but then abandoned it. As above, the robot searched for another helper. helpful: the human was interested, followed the robot to the location of the problem, and assisted the robot. In these cases, the robot was able to finish the task. (a) Going through a blocked gate (b) Picking up an inaccessible box (c) Visiting a missing target Fig. 1.3. The human-robot interaction experiments setup We purposefully constrained the environment used in the experiments to encourage human-robot interaction, as follows: Traversing blocked gates: the robot’s task was to pass through a gate formed by two closely placed colored targets (Figure 1.3(a)), but its path was blocked by a large box. The robot expressed its intentions by executing the Track behavior, making its way around one of the targets. Trying to reach the desired distance and angle to the target while being hindered by box resulted in its clear manifestation of the direction it wanted to pursue, blocked by the obstacle. Moving inaccessible located objects: the robot’s task was to pick up a small object which was made inaccessible by being placed in a narrow space between two large boxes (Figure 1.3(b)). The robot expressed its intentions by attempting to execute the PickUp behavior, lowering and opening its gripper and tilting its camera downward while approaching the object, and then moving backwards to avoid the boxes. 1.5 Learning from Imitation and Additional Cues 7 Visiting non-existing targets: the robot’s task was to visit a number of targets in a specific order (Green, Orange, Blue, Yellow, Orange, Green), in an environment where one of the targets had been removed (Figure 1.3(c)). After some time, the robot gave up searching for the missing target and sought out a human helper. The robot expressed its intentions by searching for a target, which appeared as aimless wandering. This behavior was not conducive for the human to infer the robot’s goal and problem. In this and similar situations, our framework would benefit from more explicit communication. 1.4. 2 Discussion From the experimental results [18] and the interviews and report of the human subject who interacted with the robot, we derived the following conclusions about the robot’s social behavior: Capturing a human’s attention by approaching and then going back-and-forth is a behavior typically easily recognized and interpreted as soliciting help. Getting a human to follow by turning around and starting to go to the place where the problem occurred (after capturing the human’s attention) requires multiple trials in order for the human to follow the robot the entire way. Even if interested and realizing that the robot wants something from him, the human may have trouble understanding that following is the desired behavior. Also, after choosing to follow the robot, if wandering in search of the place with the problem takes too long, the human gives up not knowing whether the robot still needs him. Conveying intentions by repeating a failing behavior in front of a helper is effective for tasks in which the task components requiring help are observable to the human (such as the blocked gate). However, if some part of the task is not observable (such as the missing target), the human cannot infer it from the robot’s behavior and thus is not able to help (at least not without trial and error). 1.5 Learning from Imitation and Additional Cues Learning by observation and imitation are especially effective means of human skill acquisition. As skill or task complexity increases, however, teaching typically involves increased concurrent use of multiple instructional modalities, including demonstration, verbal instruction, attentional cues, and gestures. Students/learners are typically given one or a few demonstrations of the task, followed by a set of supervised practice trials. During those, the teacher provides feedback cues indicating needed corrections. The teacher may also provide additional demonstrations 8 Task Learning Through Imitation and Human-Robot Interaction that could be used for generalization. While most of these learning and teaching tools are typically overlooked in the majority of robot teaching approaches, considering them collectively improves the imitation learning process considerably. Toward this end, we developed a method for learning representations of high level tasks. Specifically, we augmented imitation learning by allowing the demonstrator to employ additional instructive activities (verbal commands and attentional cues) and by refining the learned representations through generalization from multiple learning experiences and through direct feedback from the teacher. In our work, the robot is equipped with a set of skills in the form of behaviors [13, 14]; we focus on a strategy that enables it to use those behaviors to construct a high-level task representation of a novel complex, sequentially structured task. We use learning by experienced demonstrations in which the robot actively participates in the demonstration provided by the teacher, experiencing the task through its own sensors, an essential characteristic of our approach. We assume that the teacher knows what behaviors the robot has, and also by what means (sensors) the robot can perceive demonstrations. The advantage of putting the robot through the task during the demonstration is that the robot is able to adjust its behaviors (via their parameters) using the information gathered through its own sensors: the values of all behaviors’ parameters are learned direct