The primary responsibility of all scientists is to ensure the integrity of their work. For cognitive and social scientists, this means first and foremost preserving clarity about what we know about people, and not allowing descriptions of technology to demean or obscure the reality of how people think, behave, and live. Without this clarity, engineering requirements analyses, tool design, and evaluations of people will be confused. A sharp, uncompromising understanding about the nature of people is essential if we are to design and fit new technologies that are appropriate and successful for NASA's mission operations

Clancey, William J.

NASA Technical Reports Server

Source of Acquisition 
NASA Ames Research Center 
ea es: 
William 3: Clancey 
NASA Ames Research Center 
Intelligent Systems Division, MS 269-3 
bfoffett Field, CA 9403 5 
William. J.Clancey@NASA.gov 
Abstract 
The primary responsibility of all scientists IS to ensure the 
integrity of their work. For cogni t ivehd social scientists, 
this means first and foremost preserving clarity about what 
we know about people, and not allowing descriptions of 
technology to demean or obscure the reality of how people 
think, behave, and live. Without [his clarity, engineering 
requirements analyses, tool design, and evaluations of 
people will be confused. A sharp, uncompromising 
understanding about the nature of people is essential if we 
are to design and fit new technologies that are appropriate 
and successful for NASA’s mission operations. 
Introduction: How scientists talk about robots 
Scientists, in conversing with the public, as well as each 
other, often use anthropomorphic terms that are not 
appropriate for today’s machines. For example, the initial 
NASA press release about the Mars Exploration Rover 
(MER) stated: 
July 28, 2000 -- In 2003, NASA plans to launch a 
relative of the now-famous 1997 Mars Pathfinder 
rover. Using drop, bounce, and roll technology, this 
larger cousin is expected to reach the surface of the 
Red Planet in January, 2004 .... This new robotic 
explorer will be able to trek up to 110 yards (100 
meters) across the surface each Martian day ..., “This 
mission will give us the first ever robotjield geologist 
on Mars...” said Scott Hubbard, Mars Program 
Director at NASA Headquarters. (NASA 2000, italics 
added) 
Besides casting the machinery as part of a biological 
famiIy, this statement described the new Mars rover with 
phrases that would appear throughout the five year (and 
continuing) mission: “an explorer” and “a field geologist.” 
The official Jet Propulsion Laboratory web site announced 
when the first rover landed: “Spirit Lands On Mars and 
Sends Postcards - A traveling robotic geologist from 
NASA has landed on Mars and returned stunning images 
of  the area around its landing site in Gusev Crater ...”( JPL 
Press Release 2004-003). Posted mission reports regularly 
ascribe machine initiative to remotely controlled actions: 
“Spirit collected additional imagery of the right front 
wheel.”’ 
The confusion, of course, is that people are exploring 
Mars using robots: The machines have no scientific goals 
(let alone personal interests, including survival), no ability 
to use time wisely on any given day (let alone plan the 
mission strategically), no ability to decide what data to 
record (let alone perceive meaningful distinctions in the 
photos and measurements), and no ability to relate data to 
geological or biological hypotheses (let alone conceive 
new interpretations). 
Referring to the robots as if they are people has negative 
implications for the space program. For example, the MER 
principal investigator, Steve Squyres, stated at the NASA 
Symposiuin on Risk and Exploration: 
We are very far away from being able to build robots 
- I’m not going to see it in my lifetime - that have 
anything like the capabilities that humans will have to 
explore, let alone to inspire. And when I hear people 
point to Spirit and Opportunity and say that these are 
examples of why we don‘t need to send humans to 
Mars, I get very upset. Because that’s not even the 
right discussion to be having. We must send humans 
to Mars. We can’t do it soon enough for me. (Dick 
and Cowing 2005) 
Squyres aptly states the facts: “The rovers are our 
surrogates, our robotic precursors to a world that, as 
humans, we’re still not quite ready to visit” (Squyres 2005, 
p. 378). Indeed, each MER is a kind of mobile laboratory, 
including a microscope, spectrometers, and cameras, that 
are remotely controlled by computer programs laboriously 
When something goes wrong or a problem is solved, 
attention shifts to the people behind the scenes: “The 
operations team has successfully commanded Spirit to 
drive using only 5 wheels” (Spirit Stahis for sol 778-783; 
Release Date: 3/16/06). 
https://ntrs.nasa.gov/search.jsp?R=20060052409 2019-08-30T00:03:13+00:00Z
designed and transmitted fi-om Pasadena, California to 
Mars every day. 
One might rationalize the terminological problems with 
MER by excusing the journalists and public affairs 
officers, because only engineers should be expected to use 
precise, scientific terms for describing machines. Of 
course, the history of artificial intelligence (AI) research 
demonstrates the contrary-computer scientists have 
notoriously used anthropomorphic language to describe 
their programs (McDermott 1976). Thus, we have so- 
called expert systems, intelligent tutoring systems, and 
autonomous agents. More recently, the human-robotic 
interaction (HRI) community often uses the terms 
“collaboration” and “teamwork” with little discriminating 
consideration of what these words mean in human affairs 
(Clancey 2004). = 
Although one might meaningfully refer to a team of 
robots, as we refer to a team of oxen, or even a human- 
robot team playing a ball game (Argall 2006), some HRI 
researchers group the robots with the person in control, as 
if driving an ox cart were a “collaboration” between the 
driver and beasts: “Rather than viewing robots as semi- 
autonomous tools that are directed via human supervision, 
we envision robots that can cooperate with humans as 
capable partners” (Braezael, et al. 2004, p. 2). 
Engineers are of course entitled to idealistic visions, but 
imagination and reality are confused in descriptions of 
current technology in scientific papers and official press 
releases alike. Instead of disciplined descriptions (what is a 
partner?), a free-for-all has developed that is 
indistinguishable from mass advertising, as in this blurb for 
AIBO, a robot modeled after a dog: “a unique companion, 
gradually adapting itself to your environment, capable of 
expressing emotion, very skilful and with an inherent 
desire to entertain” (Sony 2006). 
A Human-Centered Approach 
Science and engineering descriptions of machines must 
respect and be grounded in scientific studies of people. In 
developing technology for practical purposes, a balance 
must be struck by the creative motive to replicate nature 
(“biologically inspired” invention) and the realistic 
requirement to provide useful, cost-effective tools. 
In either case, whether pursuing engineering for its own 
sake or to solve a practical problem (e.g., making lunar 
exploration safer and more effective), an engineer cannot 
rigorously use terms like “explorer,” “geologist,” 
“collaboration,” “teamwork,” as a specification for 
machine behavior without understanding what these terms 
mean in human affairs. This approach is often called 
“human-centered computing” (HCC) because it grounds 
both AI research and AI-based tool development in the 
study of human practices and needs (Hoffman, Hayes, and 
Ford 2001). 
The starting point for I-ICC is clear thinking about the 
differences between people and current technology. If we 
start instead with an inflated view of machines, we get a 
diminished view of people, and the design process focuses 
instead on mitigating human failures. Thus, fantasized, 
idealized machines become the yardstick for critiquing 
human work and reason. 
HCC research aims to be scientific by treating the 
differences between people and machines with integrity. 
We call certain programs “model-based“ rather than 
“knowledge-based”-because they contain models, not 
knowledge. We distinguish between coordination, 
cooperation, and collaboration (Clancey 2004). We call 
MER robots laboratories, not geologists. 
The nature of the MER rovers should not be confusing 
or difficult to describe, because aside from some path- 
planning and obstacle avoidance, they do not take any 
actions without being explicitly programmed (Squyres 
2005; Hubbard 2005). Further, MER does not even use 
1970s model-based inference to post hypotheses and plan 
behaviors. Thus there is no question about whether the 
rovers reason or judge, for they do not make any decisions 
about the scientific work. 
When we do deploy a system with model-based 
planning and “self-programming,” you can be sure 
scientists and engineers wiIl refer to the rovers as forming 
and testing hypotheses, just as they have described expert 
systems. But model-based machines have no ability to 
conceive ideas; they can classify data, relate 
categorizations to action plans, and sequence and compose 
abstract plans into detailed programs. Such manipulations 
of “semantic networks” provide a mechanism that is quite 
limited compared to how human conceptualization 
dynamically adapts and re-coordinates perceptual-motor 
categories and sequences (Clancey 1997). 
To develop machines that can collaborate or even be 
servants, we need to understand these human relations, 
roles, and practices. Even a servant must interpret 
commands with respect to a social background, and adapt 
to unexpected circumstances, especially when 
unsupervised. What are the underlying perceptual and 
conceptual capabilities that make trust, teamwork, and 
collaboration possible? I have argued (Clancey 2004) that 
higher-order consciousness, with a‘dynamic concept of the 
self, inherently socially oriented, is necessary to 
collaborate in projects (e.g., scientific exploration, 
constructing a lunar outpost). 
On the other hand, it is questionable whether an 
astronaut would actually want a robot that viewed itself as 
being a peer. This would mean for example, that the robot 
had the right to leave the project and go work for someone 
else, or to prove to people that it is capable of doing 
independent work, etc. 
Furthermore, if robots are truly so capable, and cheaper 
and safer than sending people to Mars, why aren’t we using 
them in coal mines, rather than endangering human lives? 
Surely it would cost less to develop a robotic miner and 
send it into a mine than to send a robotic geologist to Mars. 
But unfortunately, human lives cost less than robotic 
miners; and interplanetary probes cost less than hnman 
space flight. 
~~~~~~~~~~~ 
U‘hat is the effect of human-robot irfleraction research on 
human culture? As for earlier technological metaphors 
applied to people (e.g., the brain is a telephone 
switchboard), the concept of “humanoid robot” is 
reductionist, serving to focus but also stall engineering. In 
particular, in the realm of space exploration, the idea of 
humanoid automation has deflected attention from a 
broader range of machinery (e.g., drills, automated trucks) 
and sensing and monitoring systems (e.g., for life support). 
For the public, and perhaps indeed many scientists, the 
“robotic geologist” and “robotic explorer” metaphor has 
been taken literally, to mean that we do not need to send 
people to explore Mars, for we already have robotic 
explorers on the surface. This confusion extends to a kind 
of “Wizard of Oz” phenomenon: Transfixed by the images 
of Mars, we mythologize “the little rover that could”; and 
few attempt to grasp the complexity of the scientists’ work 
“behind the curtain.” 
On the plus side, for those with a critical eye towards 
idealistic and poetic talk, the existence of these robotic 
tools provides concrete examples for clear speaking about 
machines: We can articulate better what we mean by 
identity, consciousness, society, responsibility, 
commitment, etc. by contrasting everyday human 
experience with the now starkly simpler and visibly 
determined, programmed behavior of these robots. And we 
can visualize better metaphors to guide engineering, such 
as a “robotic burro” (Hirsh et al. in press). 
In conclusion, I believe that the primary responsibility of 
all scientists is to ensure the integrity of their work. For 
cognitive and social scientists, this means first and 
foremost preserving clarity about what we know about 
people, and not allowing descriptions of technology to 
demean or obscure the reality of how people think, behave, 
and live. Without this clarity, engineering requirements 
analyses, tool design, and evaluations of people will be 
confused. A sharp, uncompromising understanding about 
the nature of people is essential if we are to design and fit 
new technologies that are appropriate and successful for 
NASA’s mission operations. 
And finally, let us not become lost in an AI researcher’s 
Pygmalion dream. When asked why we don’t simply send 
robots to Mars when they eventually are more capable, 
rather than send people at greater expense sooner, Chris 
McKay, a NASA planetary scientist replied, “Even if 
computers progress so much that they can go to Paris, taste 
the wine, eat the food and come back and tell me all about 
it, I’d still want to go myself.” 
References 
Argall, B., Yang Gu, Y., and Browning, B. 2006. The First 
Segway Soccer Experience: Towards Peer-to-Peer 
Human-Robot Teams. HRI’06, Salt Lake City, UT. 
Breazeal, C., Brooks, A., ChiIongo, D., Gray, J., Hoffman, 
G., Kidd, C., Lee, K., Lieberman, J., and Loclcerd A. 
2004. Working Collaboratively with Humanoid Robots. 
Proceedings of Hunzanoids 2004, Los Angeles, CA. 
Clancey, W. J. 1997. Situated Cognition. New York: 
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Clancey, W. J. 2004. Roles for Agent Assistants in Field 
Science: Personal Projects and Collaboration. IEEE 
Transactions on Systems, Man, and Cybernetics, Part C: 
Applications and Reviews 34(2):125-137. 
Dick, S .  J. and Cowing, K. L. eds. 2004. Proceedingsfiom 
the NASA Administrator‘s S’vnposium: “Risk and 
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Hirsh, R., Graham, J., Tyree, K., Sierhuis, M., and 
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Hoffman, R. R., Hayes, P. J., and Ford, K. M. 2001. 
Human-Centered computing: Thinking in and outside 
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Hubbard, G. Scott. 2005. Humans and Robots: Hand in 
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McDermott, D. 1976. Artificial intelligence meets natural 
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Clear Speaking about Machines: People are Exploring Mars, Not Robots

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