Planning for Persistent Autonomy: Where are we struggling ? Daniele Magazzeni King’s College London

Artificial Intelligence Planning Group at King’s We have a rich portfolio of planning for real applications, with companies and organisations: Autonomous Underwater Vehicles Energy Technology Autonomous Drones and UAVs Ocean Liners Multiple Battery System Management Hybrid Vehicles Smart Buildings Air Traffic Control and Plane Taxiing Urban Traffic Control

Planning with Robots Planning for Persistent Underwater Autonomy Policy Learning for Autonomous Feature Tracking Autonomous maintenance of submerged oil & gas infrastructures EU Project PANDORA Policy Learning for Autonomous Feature Tracking.  Autonomous Robots. (2015) Toward Persistent Autonomous Intervention in a Subsea Panel.  Autonomous Robots. (2016) Opportunistic Planning in Autonomous Underwater Missions.  IEEE Transactions on Automation Science and Engineering. (2017)

Planning with Robots Robot interacting with children in a toy cleaning scenario -localisation and navigation in a crowded and changing scene  -iterative task planning in an open world -engaging with multiple users in a dynamic collaborative task Robotics Receptionist at King’s College (with Elizabeth Sklar and Simon Parsons) Goal: to deliver an advanced yet flexible space autonomous software framework/system suitable for single and/or collaborative space robotic means/missions

Focus of Our Research Rich planning models Validation Integration We are pushing the research on planning with hybrid systems PDDL+ modelling Planners (UPMurphi, DiNO, SMTPlan+) Policy learning framework Planning with external solvers Validation We explore the links between planning and verification Plan validation (VAL) Plan robustness evaluation Domain validation Integration Planning with ROS

Agnostic about the planning system Modular Open source and free

Domain/Plan Correctness/Robustness I believe that in planning we are too optimistic about the assumptions we can make. Q1: what are reasonable assumptions we can make when writing our domains? (relocation, precise sensing, actuator precision) Plans are correct-by-construction, modulo the correctness of the model. Q2: can we do domain validation, and what does it mean in robotics domain? In persistent autonomy, plan validity is affected by temporal uncertainty due to uncertain and dynamic environment. Q3: how can we evaluate plan temporal robustness? Planning community is making great progress in handling very rich planning models (PDDL+, external solver, semantic attachments) Q4: how can we leverage rich domain modelling to model robot dynamics? Do robotics people think it's important to model dynamics in the planning model?

What should I plan for ? We often (always?) assume to have goals. I'd like the robot to collaborate in deciding upon its own goals. Based on: -HRI -Curiosity and exploration -Motivations -Need for recovering. Problem awareness -Improving its own domain model. (I'll get back in one hour and I want to see a more concrete domain file). Q5: are there other factors the Robot should check for deciding goals? Q6: how are we doing (really) with this issue?

Human-Robot Interaction (not the standard one…) In many cases, policies request humans to approve plans before execution: Q7: how can we make plans clear to humans? (not PDDL.. , non domain-specific approach, instruction graphs) If the operator cannot approve the plan, perhaps he/she could approve a slightly different plan. Q8: how can we effectively handle plan execution with human interaction? Humans can decide to take less/more risk (e.g., for getting less/more reward) Q9: should we be generating sets of plans, rather than a single plan?

Integration Good progress so far, but still a lot to do. Q10: where should we focus? When planning for long-horizon missions, scalability is a huge issue. Q11: is it possible to create a model/planning solution that is detailed, but becomes gradually abstract further into the future? Can such a solution be integrated, and handled at execution time? Q12: can we share benchmarks and data sets?

Daniele Magazzeni Thank you! BTW: we are hiring!

(non-exhaustive) list… Q1: what are reasonable assumptions we can make when writing our domains? Q2: can we do domain validation, and what does it mean in robotics domain? Q3: how can we evaluate plan temporal robustness? Q4: how can we leverage rich domain modelling to model robot dynamics? Do robotics people think it's important to model dynamics in the planning model? Q5: are there other factors the Robot should check for deciding goals? Q6: how are we doing (really) with this issue? Q7: how can we make plans clear to humans? (not PDDL.. , non domain-specific approach, instruction graphs) Q8: how can we effectively handle plan execution with human interaction? Q9: should we be generating sets of plans, rather than a single plan? Q10: where should we focus? Q11: is it possible to create a model/planning solution that is detailed, but becomes gradually abstract further into the future? Can such a solution be integrated, and handled at execution time? Q12: can we share benchmarks and data sets?