Combining high-level causal reasoning witth low-level geometric reasoning and motion planning for robotic manipulation

We present a modular planning framework for manipulation tasks that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. This framework features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning. 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. The geometric reasoner guides the causal reasoner to find feasible kinematic solutions by means of external predicates/functions. We show the applicability of this method on two sample problems: extended towers of Hanoi and multiple robot manipulation inside a maze. We focus on two main problems in this planning framework: i) a systemic analysis of various levels of integration between high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning and ii) generalization of the planning framework to continuous domains. For the former, we consider various levels of integration in the two domains mentioned above, to check which level of integration achieves better performance. For the latter, we abstract configurations at the representation level by continuous regions instead of discrete positions, and introduce an incremental sampling-based method coupled to a goal region-based probabilistic path planner for extracting specific goal configurations required for generating valid plans for execution. This way, we tightly integrate high-level reasoning and region-based motion planning and provide a general framework for addressing a wide spectrum of manipulation problems

Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version

Integrated task and motion planning using physics-based heuristics

—This work presents a knowledge-based task and motion planning framework based on a version of the FastForward task planner. A reasoning process on symbolic literals in terms of knowledge and geometric information about the workspace, together with the use of a physics-based motion planner, is proposed to evaluate the applicability and feasibility of manipulation actions and to compute the heuristic values that guide the search. The proposal results in low-cost physically-feasible plansPeer ReviewedPostprint (published version

Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments

Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.Comment: 12 pages, 6 figures, accepted for publication on IEEE Transactions on Cybernetics in March 202

Automating adaptive execution behaviors for robot manipulation

Robotic manipulation in semi-structured and changing environments requires systems with: a) perception and reasoning capabilities able to capture and understand the state of the environment; b) planning and replanning capabilities at both symbolic and geometric levels; c) automatic and robust execution capabilities. To cope with these issues, this paper presents a framework with the following features. First, it uses perception and ontology-based reasoning procedures to obtain the Planning Description Domain Language files that describe the manipulation problem at task level. This is used in the planning stage as well as during task execution in order to adapt to new situations, if required. Second, the proposed framework is able to plan at both task and motion levels, intertwining them by incorporating geometric reasoning modules to determine some of the symbolic predicates needed to describe the states. Finally, the framework automatically generates the behavior trees required to execute the task. The proposal takes advantage of the ability of behavior trees to be edited during run time, allowing adaptation of the action plan or of the trajectories according to changes in the state of the environment. The approach allows for robot manipulation tasks to be automatically planned and robustly executed, contributing to achieve fully functional service robots.Peer ReviewedPostprint (published version

Task planning using physics-based heuristics on manipulation actions

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge. The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.Peer ReviewedPostprint (author's final draft

Geometric Reasoning for Automated Planning

An important aspect of mission planning for NASA s operation of the International Space Station is the allocation and management of space for supplies and equipment. The Stowage, Configuration Analysis, and Operations Planning teams collaborate to perform the bulk of that planning. A Geometric Reasoning Engine is developed in a way that can be shared by the teams to optimize item placement in the context of crew planning. The ISS crew spends (at the time of this writing) a third or more of their time moving supplies and equipment around. Better logistical support and optimized packing could make a significant impact on operational efficiency of the ISS. Currently, computational geometry and motion planning do not focus specifically on the optimized orientation and placement of 3D objects based on multiple distance and containment preferences and constraints. The software performs reasoning about the manipulation of 3D solid models in order to maximize an objective function based on distance. It optimizes for 3D orientation and placement. Spatial placement optimization is a general problem and can be applied to object packing or asset relocation