King’s College London https://nms.kcl.ac.uk/daniele.magazzeni/ Artificial Intelligence Planning for Autonomous Systems: where are we struggling ? Daniele Magazzeni King’s College London https://nms.kcl.ac.uk/daniele.magazzeni/

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 Efficient Macroscopic Urban Traffic Models for Reducing Congestion: A PDDL+ Planning Approach.  AAAI 2016. Plan-based Policies for Efficient Multiple Battery Load Management. JAIR 2012 Solving Realistic Unit Commitment Problems Using Temporal Planning: Challenges and Solutions. ICAPS 2016

Planning with Robots Planning for Persistent Underwater Autonomy Policy Learning for Autonomous Feature Tracking Autonomous maintenance of submerged oil & gas infrastructures EU Project PANDORA EU Project SQUIRREL Robot interacting with children in a toy cleaning scenario 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)

AI Planning Given Find How An initial state: a set of propositions and assignments to numeric variables A goal: a desired set of propositions/assignments A set of actions each with: Preconditions on execution Effects that describe how the world changes upon their execution Find A sequence of actions (a plan) that when applied in the initial state leads to a state that satisfies the goal condition How Use heuristics to prune the state space and guide the search Standard Description Language (PDDL) Domain-Independent Solvers (:action navigate :parameters (?r – rover ?x ?y - waypoint) :precondition (and (available ?r) (at ?r ?x) (visible ?x ?y) (>= (energy ?r) 8)) :effect (and (decrease (energy ?r) 8) (not (at ?r ?x )) (at ?r ?y)))

AI Planning 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 Oil and Gas Industry All modelled using PDDL+ and differential equations Classical planning: a plan to get to a desirable state that satisfies some goals. Optimisation: minimize/maximise a cost function. Temporal planning: actions have a duration. Concurrency, synchronisation, time dependent effects. Planning with preferences: hard and soft goals. Conditional planning: actions can perform observations, and the plan contains branches. Planning for Hybrid Systems (PDDL+) Hybrid Automata semantics Discrete actions Processes modelling continuous change

Focus of Our Research Rich planning models PDDL+ modelling Planners Based on discretisation (UPMurphi) Based on SMT (SMTPlan+) Policy learning framework Planning with external solvers Validation Plan validation (VAL) Plan robustness evaluation Domain validation Explainable Planning Planning with Robots Persistent Autonomy ROSPlan UPMurphi: A Tool for Universal Planning on PDDL+ Problems. ICAPS 2010 Heuristic Planning for PDDL+ Domains.  IJCAI 2016 PDDL+ Planning via Constraint Answer Set Programming.  COPLAS 2016

Planner for Hybrid Systems based on SMT A Compilation of the Full PDDL+ Language into SMT. ICAPS 2016 Planner for Hybrid Systems based on SMT Free and open source: http://kcl-planning.github.io/SMTPlan/

Task Planning with Agnostic about the planning system Modular ROSPlan: Planning in the Robot Operating System.  ICAPS 2015 Task Planning with Agnostic about the planning system Modular Open source and free http://kcl-planning.github.io/ROSPlan/

http://kcl-planning.github.io/ROSPlan/

Dagstuhl Workshop on Planning and Robotics 16-20 January 2017 Organisers: Malik Ghallab Nick Hawes Daniele Magazzeni Brian Williams Coordinator: Andrea Orlandini Transparency in Autonomy Symbiotic Autonomy Safety in Industrial Robotics Long-Term Autonomy Task-Motion Planning Goal-Directed Autonomy

To model, or not to model, that is the question

Domain/Plan Correctness/Robustness I believe we are often too optimistic about the assumptions we can make. Q1: what are reasonable assumptions we can make when writing our models? (relocation, precise sensing, actuator precision) Plans are correct-by-construction, modulo the correctness of the model. Q2: can we do model 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? First Tutorial on planning and Robotics at ICRA-17 Workshop on Planning and Robotics at ICRA-17

Daniele Magazzeni Thank you! BTW: we are hiring!

(non-exhaustive) list… Q1: what are reasonable assumptions we can make when writing our models? Q2: can we do model 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?