Designing Environments Conducive to Interpretable Robot Behavior

Designing robots capable of generating interpretable behavior is essential for effective human-robot collaboration. This requires robots to be able to generate behavior that aligns with human expectations but exhibiting such behavior in arbitrary environments could be quite expensive for robots, and in some cases, the robot may not even be able to exhibit expected behavior. However, in structured environments (like warehouses, restaurants, etc.), it may be possible to design the environment so as to boost the interpretability of a robot's behavior or to shape the human's expectations of the robot's behavior. In this paper, we investigate the opportunities and limitations of environment design as a tool to promote a particular type of interpretable behavior - known in the literature as explicable behavior. We formulate a novel environment design framework that considers design over multiple tasks and over a time horizon. In addition, we explore the longitudinal effect of explicable behavior and the trade-off that arises between the cost of design and the cost of generating explicable behavior over an extended time horizon.

Authors

• Anagha Kulkarni 0002
• Sarath Sreedharan
• Sarah Keren
• Tathagata Chakraborti
• David E. Smith 0001
• Subbarao Kambhampati

Keywords

• Shape
• Collaboration
• Tools
• Task analysis
• Robots
• Intelligent robots
• Restaurants
• Multiple Tasks
• Environmental Design
• Design Cost
• Human-robot Collaboration
• Objective Function
• Total Cost
• Human Model
• Autonomic System
• Increase In Costs
• Distance Function
• Minimum Score
• Design Problem
• Mental Models
• Design Setting
• Optimal Plan
• Planning Model
• Overhead Costs
• Planning Problem
• Set Of Modifications
• Planning Cost
• One-time Cost
• Environment Configuration
• Running Example
• Planning Of Robots
• Original Domain