9 accepted papers
Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions,
When independently trained or designed robots are deployed in a shared environment, their combined actions can lead to unintended negative side effects (NSEs). To ensure safe and efficient operation, robots must optimize task performance while minimizing the penalties associated with NSEs, balancing
Agents often need to infer a reward function from observations to learn desired behaviors. However, agents may infer a reward function that does not align with the original intent because there can be multiple reward functions consistent with its observations. Operating based on such misaligned rewa…
Autonomous systems are often deployed in the open world where it is hard to obtain complete specifications of objectives and constraints. Operating based on an incomplete model can produce negative side effects (NSEs), which affect the safety and reliability of the system. We focus on mitigating NSE…
Safe and reliable decision-making is critical for long-term deployment of autonomous systems. Despite the recent advances in artificial intelligence, ensuring safe and reliable operation of human-aligned autonomous systems in open-world environments remains a challenge. My research focuses on develo…
Although experts carefully specify the high-level decision-making models in autonomous systems, it is infeasible to guarantee safety across every scenario during operation. We therefore propose a safety metareasoning system that optimizes the severity of the system's safety concerns and the interfer…
Agents operating in unstructured environments often create negative side effects (NSE) that may not be easy to identify at design time. We examine how various forms of human feedback or autonomous exploration can be used to learn a penalty function associated with NSE during system deployment. We f…
Reduced models allow autonomous robots to cope with the complexity of planning in stochastic environments by simplifying the model and reducing its accuracy. The solution quality of a reduced model depends on its fidelity. We present 0/1 reduced model that selectively improves model fidelity in cert…
We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem - a general framework to model path planning and decision making in stochastic environments with goal uncertainty. The framework extends the stochastic shortest path (SSP) model to dynamic environments in which it is impossible t…