Planning for Persistent Autonomy: Where are we struggling ?

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

2. Artificial Intelligence Planning Groupat 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

3. Planning with Robots Planning for Persistent Underwater Autonomy Policy Learning for Autonomous Feature Tracking Autonomous maintenance of submerged oil & gas infrastructuresEU 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)

4. 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

5. Focus of Our Research • Rich planning models • 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

6. Agnostic about the planning system • Modular • Open source and free

7. 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?

8. 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?

9. 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?

10. 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?

11. Daniele Magazzeni Thank you! BTW: we are hiring!

12. (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?