We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains. This paper is under consideration for acceptance in TPLP.
@cite_38: 
@cite_35: In recent years, domain-independent planning has been applied to a rising number of real-world applications. Usually, the description language of choice is PDDL. However, PDDL is not suited to model all challenges imposed by real-world applications. proposed semantic attachments to allow the computation of Boolean fluents by external processes called modules during planning. To acquire state information from the planning system a module developer must perform manual requests through a callback interface which is both inefficient and error-prone. In this paper, we present the Object-oriented Planning Language OPL, which incorporates the structure and advantages of modern object-oriented programming languages. We demonstrate how a domain-specific module interface that allows to directly access the planner state using object member functions is automatically generated from an OPL planning task. The generated domain-specific interface allows for a safe and less error-prone implementation of modules. We show experimentally that this interface is more efficient than the PDDL-based module interface of TFD M.
@cite_4: We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The frame-work features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper.
@cite_7: We describe an integrated strategy for planning, perception, state estimation and action in complex mobile manipulation domains based on planning in the belief space of probability distributions over states using hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of logical expressions that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators can give rise to task-oriented perception in support of the manipulation goals. An implementation of this method is demonstrated in simulation and on a real PR2 robot, showing robust, flexible solution of mobile manipulation problems with multiple objects and substantial uncertainty.
@cite_36: 
@cite_21: We propose an original approach to integrate symbolic task planning, and geometric motion and manipulation planning. We focus more particularly on one key aspect: the relation between the symbolic positions and their geometric counterparts. Indeed, we have developed an instantiation process that is able to propagate incrementally task-dependent as well as 3D environment-dependent constraints and to guide efficiently the search until valid geometric configurations are found that satisfy the plan at both levels. The overall process is discussed and illustrated through an implemented example.
@cite_42: Enabling robots to accomplish sophisticated tasks requires enhancing their capability to plan at multiple levels of discrete and continuous abstractions. Toward this goal, the proposed approach couples the ability of sampling-based motion planning to handle the complexity arising from high-dimensional robotic systems, nonlinear dynamics, and collision avoidance with the ability of discrete planning to handle discrete specifications. The approach makes it possible to specify tasks via Linear Temporal Logic (LTL) and automatically computes collision-free and dynamically-feasible motions that enable the robot to carry out assigned tasks. While discrete planning guides sampling-based motion planning, the latter feeds back information to further refine the guide and advance the search. Sampling is also used in the discrete space to shorten the length of the discrete plans and to expand the search toward new discrete states. Experiments with high-dimensional dynamical robot models performing various LTL tasks show significant computational speedups over related work.
@cite_3: Robot task planning is an inherently challenging problem, as it covers both continuous-space geometric reasoning about robot motion and perception, as well as purely symbolic knowledge about actions and objects. This paper presents a novel “knowledge of volumes” framework for solving generic robot tasks in partially known environments. In particular, this approach (abbreviated, KVP) combines the power of symbolic, knowledge-level AI planning with the efficient computation of volumes, which serve as an intermediate representation for both robot action and perception. While we demonstrate the effectiveness of our framework in a bimanual robot bartender scenario, our approach is also more generally applicable to tasks in automation and mobile manipulation, involving arbitrary numbers of manipulators.
@cite_6: 
@cite_19: We propose a constraint-based approach to address a class of problems encountered in combined task and motion planning (CTAMP), which we call kinematically constrained problems. CTAMP is a hybrid planning process in which task planning and geometric reasoning are interleaved. During this process, symbolic action sequences generated by a task planner are geometrically evaluated. This geometric evaluation is a search problem per se, which we refer to as geometric backtrack search. In kinematically constrained problems, a significant computational effort is spent on geometric backtrack search, which impairs search at the task level. At the basis of our approach to address this problem, is the introduction of an intermediate layer between task planning and geometric reasoning. A set of constraints is automatically generated from the symbolic action sequences to evaluate, and combined with a set of constraints derived from the kinematic model of the robot. The resulting constraint network is then used to prune the search space during geometric backtrack search. We present experimental evidence that our approach significantly reduces the complexity of geometric backtrack search on various types of problem.
@cite_27: We present a formal framework where a nonmonotonic formalism (the action description language @math ) is used to provide robots with high-level reasoning, such as planning, in the style of cognitive robotics. In particular, we introduce a novel method that bridges the high-level discrete action planning and the low-level continuous behavior by trajectory planning. We show the applicability of this framework on two LEGO MINDSTORMS NXT robots, in an action domain that involves concurrent execution of actions that cannot be serialized.
@cite_2: The need for combined task and motion planning in robotics is well understood. Solutions to this problem have typically relied on special purpose, integrated implementations of task planning and motion planning algorithms. We propose a new approach that uses off-the-shelf task planners and motion planners and makes no assumptions about their implementation. Doing so enables our approach to directly build on, and benefit from, the vast literature and latest advances in task planning and motion planning. It uses a novel representational abstraction and requires only that failures in computing a motion plan for a high-level action be identifiable and expressible in the form of logical predicates at the task level. We evaluate the approach and illustrate its robustness through a number of experiments using a state-of-the-art robotics simulator and a PR2 robot. These experiments show the system accomplishing a diverse set of challenging tasks such as taking advantage of a tray when laying out a table for dinner and picking objects from cluttered environments where other objects need to be re-arranged before the target object can be reached.
@cite_17: We provide a systematic analysis of levels of integration between discrete high-level reasoning and continuous low-level feasibility checks to address hybrid planning problems in robotic applications. We identify four distinct strategies for such an integration: (i) low-level checks are done for all possible cases in advance and then this information is used during plan generation; (ii) low-level checks are done exactly when they are needed during the search for a plan; (iii) low-level checks are done after a plan is computed, and then a new plan is computed if the plan is found infeasible; (iv) similar to the previous strategy of replanning but a new plan is computed subject to the constraints obtained from previous low-level checks. We perform experiments on hybrid planning problems in housekeeping domain considering these four methods of integration, as well as some of their combinations. We analyze the usefulness of different levels of integration in this domain, both from the point of view of computational efficiency (in time and space) and from the point of view of plan quality relative to its feasibility. We discuss advantages and disadvantages of each strategy in the light of experimental results.
There is a variety of recent work on hybrid planners that combine motion planning with classical planners. Some of them are based on modifications introductions of search algorithms for motion task planning @cite_21 @cite_38 @cite_7 @cite_19 @cite_3 @cite_2 , some of them are based on formal methods @cite_42 @cite_6 , and some of them are based on modification of representation of domains @cite_27 @cite_35 @cite_4 @cite_17 . is similar to the latter group, since feasibility checks are embedded in action descriptions via external atoms (in the spirit of semantic attachments in theorem proving @cite_36 ) without having to modify the classical planners or motion planners. We refer the reader to the recent studies @cite_19 @cite_17 surveying and empirically analyzing some of these approaches.