Designing autonomy consists of mapping inputs from the environment into actuator movements, representational schemas, or speech acts. There are numerous formal definitions of autonomy and intelligence in the literature [63-67], many of which arise in discussions of adjustable or dynamic autonomy . One operational characterization of autonomy that applies to mobile robots is the amount of time that a robot can be neglected, or the neglect tolerance of the robot . A system with a high level of autonomy is one that can be neglected for a long period of time without interaction. However, this notion of autonomy does not encompass Turing-type notions of intelligence that might be more applicable to representational or speech-act aspects of autonomy.
RepileeQ2 — an extremely sophisticated humanoid ;
Robota — humanoid robots as “educational toys” ;
SonyAIBO — a popular robot dog ;
(below the AIBO) A sophisticated unmanned underwater vehicle ;
Shakey — one of the ﬁrst modern robots, courtesy of SRI International, Menlo Park, CA ;
Kismet — an anthropomorphic robot with exaggerated emotion ;
Raven — a mini-UAV used by the US military ;
iCAT — an emotive robot [Phillips Research, The Netherlands];
iRobot (registered TM) PackBot (registered TM) — a robust ground robot used in military applications .
(All images used with permission.)
Autonomy is not an end in itself in the field of HRI, but rather a means to supporting productive interaction. Indeed, autonomy is only useful insofar as it supports beneficial interaction between a human and a robot. Consequently, the physical embodiment and type of autonomy varies dramatically across robot platforms; see Figure 2, which shows a cross section of the very many different types of physical robots.
Perhaps the most strongly human-centered application of the concept of autonomy is in the notion of level of autonomy (LOA). Levels of autonomy describe to what degree the robot can act on its own accord. Although many descriptions of LOA have been seen in the literature, the best and most widely-cited description is by Tom Sheridan . In Sheridan’s scale, there is a continuum from the entity being completely controlled by a human (i.e. teleoperated), through the entity being completely autonomous and not requiring input or approval of its actions from a human before taking actions:
- Computer offers no assistance; human does it all
- Computer offers a complete set of action alternatives
- Computer narrows the selection down to a few choices
- Computer suggests a single action
- Computer executes that action if human approves
- Computer allows the human limited time to veto before automatic execution
- Computer executes automatically then necessarily informs the human
- Computer informs human after automatic execution only if human asks
- Computer informs human after automatic execution only if it decides to
- Computer decides everything and acts autonomously, ignoring the human
Variations of this scale have been developed and used by various authors [78, 79]. Importantly, Miller and Parasuraman have noted that such scales may not be applicable to an entire problem domain but are rather most useful when applied to each subtask within a problem domain . The authors further suggest that previous scales actually represent an average over all tasks.
While such (average) scales are appropriate to describe how autonomous a robot is, from a human-robot interaction point of view, a better way to consider autonomy is by describing to what level the human and robot interact and the degree to which each is capable of autonomy.
The scale presented in Figure 3 gives an emphasis to mixed-initiative interaction, which has been defined as a “flexible interaction strategy in which each agent (human and [robot]) contributes what it is best suited at the most appropriate time” . Various and different HRI issues arise along this scale. On the direct control side, the issues tend toward making a user interface that reduces the cognitive load of the operator. On the other extreme of peer-to-peer collaboration, issues arise in how to create robots with the appropriate cognitive skills to interact naturally or efficiently with a human.
Matt Johnson — collaborative control and coactive design
Note that in order for the robot to achieve peer-to-peer collaboration, it must indeed be able to flexibly exhibit “full autonomy” at appropriate times. Moreover, it may need to support social interactions. As a result, peer-to-peer collaboration may be considered more difficult to achieve than full autonomy.
Autonomy is implemented using techniques from control theory, artificial intelligence, signal processing, cognitive science, and linguistics. A common autonomy approach is sometimes referred to as the sense-plan-act model of decision-making . This model has been a target of criticism  and sometimes rightfully so, but much of the criticism may be a function of the early capacities of robots such as Shakey  rather than failings of the model per se. This model is typified by artificial intelligence techniques, such as logics and planning algorithms . The model can also incorporate control theoretic concepts, which have been used very successfully in aviation, aeronautics, missile control, and etc. (see, for example, ).
In the mid-1980s, Brooks, Arkin, and others revolutionized the field of robotics by introducing a new autonomy paradigm that came to be known as behavior-based robotics. In this paradigm, behavior is generated from a set of carefully designed autonomy modules that are then integrated to create an emergent system [17, 18, 85]. These modules generate reactive behaviors that map sensors directly to actions, sometimes with no intervening internal representations. This model for behavior generation was accompanied by hardware development that allowed autonomy modules to be implemented in the small form factors required for many robotics applications.
Today, many researchers build sense-think-act models on top of a behavior-based substrate to create hybrid architectures . In these systems, the low-level reactivity is separated
from higher level reasoning about plans and goals . Some have developed mathematics and frameworks that can be viewed as formalizations of hybrid architectures and which are referred to as theories of intelligent control [63, 87]. Interestingly, some of the most challenging problems in developing (hybrid) behaviors is in producing natural and robust activity for a humanoid robot [19, 33, 88].
Complementing the advancement of robotic control algorithms has been the advancement of sensors, sensor-processing, and reasoning algorithms. This is best represented by the success of the field of probabilistic robotics, typified by probabilistic algorithms for localization and mapping [89, 90]. It is no overstatement to say that these algorithms, which frequently exploit data from laser and other range finder devices, have allowed autonomy to become truly useful for mobile robots , especially those that require remote interaction through periods of autonomous behavior and autonomous path planning [52, 92-95]. Although probabilistic algorithms can be computationally expensive, the memory capacity, computational speed, and form factor of modern computers have allowed these algorithms to be deployable.
The areas of representing knowledge and performing reasoning, especially in team contexts, have also grown. Example developments include the emergence of belief-desire-intention architectures , joint intention theory , affect-based computing [16, 29, 98], and temporal logics.