Robot Task Planning Under Local Observability

Real-world robot task planning is intractable in part due to partial observability. A common approach to reducing complexity is introducing additional structure into the decision process, such as mixed-observability, factored states, or temporally-extended actions. We propose the locally observable Markov decision process, a novel formulation that models task-level planning where uncertainty pertains to object-level attributes and where a robot has subroutines for seeking and accurately observing objects. This models sensors that are range-limited and line-of-sight—objects occluded or outside sensor range are unobserved, but the attributes of objects that fall within sensor view can be resolved via repeated observation. Our model results in a three-stage planning process: first, the robot plans using only observed objects; if that fails, it generates a target object that, if observed, could result in a feasible plan; finally, it attempts to locate and observe the target, replanning after each newly observed object. By combining LOMDPs with off-the-shelf Markov planners, we outperform state-of-the-art-solvers for both object-oriented POMDP and MDP analogues with the same task specification. We then apply the formulation to successfully solve a task on a mobile robot.

Authors

• Max Merlin
• Shane Parr
• Neev Parikh
• Sergio Orozco
• Vedant Gupta
• Eric Rosen
• George Konidaris 0001

Keywords

• Uncertainty
• Simultaneous localization and mapping
• Navigation
• Object oriented modeling
• Sensor phenomena and characterization
• Planning
• Space exploration
• Local Observations
• Planning Process
• Target Object
• Object Properties
• Markov Decision Process
• Mobile Robot
• Range Of Sensors
• Partial Observation
• Model Assumptions
• State Variables
• Object Location
• Number Of Objects
• Reward Function
• Objective Conditions
• Transition Function
• Objects In The Scene
• Additional Objective
• Observation Space
• Efficient Planning
• Real Robot
• Occluded Objects
• Peanut Butter
• Robot Sensors
• Global Localization
• State Of Uncertainty
• List Of Objects