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Real-world robotic tasks require complex reward functions. When we define the problem the robot needs to solve, we pretend that a designer specifies this complex reward exactly, and it is set in stone from then on. In practice, however, reward design is an iterative process: the designer chooses a reward, eventually encounters an "edge-case" environment where the reward incentivizes the wrong behavior, revises the reward, and repeats. What would it mean to rethink robotics problems to formally account for this iterative nature of reward design? We propose that the robot not take the specified reward for granted, but rather have uncertainty about it, and account for the future design iterations as future evidence. We contribute an Assisted Reward Design method that speeds up the design process by anticipating and influencing this future evidence: rather than letting the designer eventually encounter failure cases and revise the reward then, the method actively exposes the designer to such environments during the development phase. We test this method in a simplified autonomous driving task and find that it more quickly improves the car's behavior in held-out environments by proposing environments that are "edge cases" for the current reward.
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The Impact of Robotics Expertise on Iterative Robot Design Decisions and Vulnerability to Design Fixation
Robot design is a complex cognitive activity that requires the designer to iteratively navigate multiple engineering disciplines and the relations between them. In this paper, we explore how people approach robot design and how trends in design strategy vary with the level of expertise of the designer. Using our interactive Build-a-Bot software tool, we recruited 39 participants from the 2022 IEEE International Conference on Robotics and Automation. These participants varied in age from 19 to 56 years, and had between 0 and 17 years of robotics experience. We tracked the participants’ design decisions over the course of a 15 min. task of designing a ground robot to cross an uneven environment. Our results showed that participants engaged in iterative testing and modification of their designs, but unlike previous studies, there was no statistically significant effect of participant’s expertise on the frequency of iterations. We additionally found that, across levels of expertise, participants were vulnerable to design fixation, in which they latched onto an initial design concept and insufficiently adjusted the design, even when confronted with difficulties developing the concept into a satisfactory solution. The results raise interesting questions for how future engineers can avoid fixation and how design tools can assist in both efficient assessment and optimization of design workflow for complex design tasks.
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- Award ID(s):
- 1845339
- PAR ID:
- 10493775
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Journal Name:
- ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE)
- ISBN:
- 978-0-7918-8734-9
- Page Range / eLocation ID:
- DETC2023-116874
- Format(s):
- Medium: X
- Location:
- Boston, Massachusetts, USA
- Sponsoring Org:
- National Science Foundation
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