RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning

Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many objects that can be interacted with, planning efficiency becomes even more important. To plan such long-horizon tasks, we present the RHH-LGP algorithm for combined task and motion planning (TAMP). First, we propose a TAMP approach (based on Logic-Geometric Programming) that effectively uses geometry-based heuristics for solving long-horizon manipulation tasks. We further improve the efficiency of this planner by a receding horizon formulation, resulting in RHHLGP. We demonstrate the effectiveness and generality of our approach on several long-horizon tasks that require reasoning about interactions with a large number of objects. Using our framework, we can solve tasks that require multiple robots, including a mobile robot and snake-like walking robots, to form novel heterogeneous kinematic structures autonomously.

[1]  Jung-Su Ha,et al.  Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image , 2020, Robotics: Science and Systems.

[2]  Jung-Su Ha,et al.  Learning Geometric Reasoning and Control for Long-Horizon Tasks from Visual Input , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Fabien Lagriffoul,et al.  Combined heuristic task and motion planning for bi-manual robots , 2018, Autonomous Robots.

[4]  Pieter Abbeel,et al.  Using Classical Planners for Tasks with Continuous Operators in Robotics , 2013 .

[5]  Esra Erdem,et al.  Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  Marc Toussaint,et al.  Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning , 2018, Robotics: Science and Systems.

[7]  Oussama Khatib,et al.  KABouM: Knowledge-Level Action and Bounding Geometry Motion Planner , 2018, J. Artif. Intell. Res..

[8]  Dylan Hadfield-Menell,et al.  Sequential quadratic programming for task plan optimization , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[9]  Achim Menges,et al.  Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Esra Erdem,et al.  Integrating hybrid diagnostic reasoning in plan execution monitoring for cognitive factories with multiple robots , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Marc Toussaint,et al.  Learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation , 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Jan Rosell,et al.  Task planning using physics-based heuristics on manipulation actions , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[13]  Bernhard Nebel,et al.  Integrating symbolic and geometric planning for mobile manipulation , 2009, 2009 IEEE International Workshop on Safety, Security & Rescue Robotics (SSRR 2009).

[14]  Leslie Pack Kaelbling,et al.  Integrated Task and Motion Planning , 2020, Annu. Rev. Control. Robotics Auton. Syst..

[15]  Marc Toussaint,et al.  Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planning , 2015, IJCAI.

[16]  Manuel Lopes,et al.  Multi-bound tree search for logic-geometric programming in cooperative manipulation domains , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Leslie Pack Kaelbling,et al.  FFRob: An Efficient Heuristic for Task and Motion Planning , 2015, WAFR.

[18]  Sergey Levine,et al.  Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning , 2019, CoRL.

[19]  Hector Geffner,et al.  Combined Task and Motion Planning as Classical AI Planning , 2017, ArXiv.

[20]  Emanuele Menegatti,et al.  Receding Horizon Task and Motion Planning in Dynamic Environments , 2020, ArXiv.

[21]  Chelsea Finn,et al.  Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation , 2019, ICLR.

[22]  Rachid Alami,et al.  A Hybrid Approach to Intricate Motion, Manipulation and Task Planning , 2009, Int. J. Robotics Res..

[23]  Nicholas Roy,et al.  Admissible Abstractions for Near-optimal Task and Motion Planning , 2018, IJCAI.

[24]  Paulo Tabuada,et al.  SMC: Satisfiability Modulo Convex Programming , 2018, Proceedings of the IEEE.

[25]  Petter Ögren,et al.  Towards Blended Reactive Planning and Acting using Behavior Trees , 2016, 2019 International Conference on Robotics and Automation (ICRA).

[26]  Fabien Lagriffoul,et al.  Combining task and motion planning: A culprit detection problem , 2016, Int. J. Robotics Res..

[27]  Pieter Abbeel,et al.  Combined task and motion planning through an extensible planner-independent interface layer , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[28]  Swarat Chaudhuri,et al.  Incremental Task and Motion Planning: A Constraint-Based Approach , 2016, Robotics: Science and Systems.

[29]  Marc Toussaint,et al.  Hierarchical Task and Motion Planning using Logic-Geometric Programming ( HLGP ) , 2010 .