I develop embodied agents that reason about how their present actions reshape the future. Every interaction changes object arrangement, free space, and accessibility, and those changes accumulate: a robot can complete each task it is given while gradually leaving behind a workspace that makes every later task harder. My work formalizes the missing capability as anticipatory planning and builds it into planners we already trust. Full details are in my research statement.
Most deployed planners are myopic: they solve each task in isolation and, lacking any model of what will be asked next, introduce side effects on future tasks which are not yet assigned. We introduce anticipatory planning objective that learns the impact of an action on future tasks while solving a current one. We validate our objective in a symbolic-level task planning domain.
Continuous parameters, such as an object's pose, determine whether an environment stays accessible or becomes cluttered and obstructed for future tasks. Anticipatory task and motion planning chooses these parameters to preserve access and reduce clutter over long-lived operation.
Real-world deployments often take place in shared environments. A staging location that is convenient for one agent may block another robot's retrieval path, while behavior that appears efficient for the robot may inconvenience those nearby. Courteous anticipation accounts for how an action affect other agents, enabling planning that balances task performance with the accessibility and needs of other agents sharing the environment.