Combined heuristic task and motion planning for bi-manual robots

Planning efficiently at task and motion levels allows the setting of new challenges for robotic manipulation problems, like for instance constrained table-top problems for bi-manual robots. In this scope, the appropriate combination of task and motion planning levels plays an important role. Accordingly, a heuristic-based task and motion planning approach is proposed, in which the computation of the heuristic addresses a geometrically relaxed problem, i.e., it only reasons upon objects placements, grasp poses, and inverse kinematics solutions. Motion paths are evaluated lazily, i.e., only after an action has been selected by the heuristic. This reduces the number of calls to the motion planner, while backtracking is reduced because the heuristic captures most of the geometric constraints. The approach has been validated in simulation and on a real robot, with different classes of table-top manipulation problems. Empirical comparison with recent approaches solving similar problems is also reported, showing that the proposed approach results in significant improvement both in terms of planing time and success rate.Peer ReviewedPostprint (author's final draft

Perceiving and acting out of the box

This paper discusses potential limitations in learning in au-tonomous robotic systems that integrate several specialized subsystemsworking at different levels of abstraction. If the designers have antici-pated what the system may have to learn, then adding new knowledgeboils down to adding new entries in a database and/or tuning parametersof some subsystem(s). But if this new knowledge does not fit in prede-fined structures, the system can simply not acquire it, hence it cannot“think out of the box” designed by its creators. We show why learningout of the box may be difficult in integrated systems, hint at some exist-ing potential approaches, and finally suggest that a better approach maycome by looking at constructivist epistemology, with focus on Piaget’sschemas theory

Multi Vehicle Routing with Nonholonomic Constraints and Dense Dynamic Obstacles

We introduce a variant of the multi-vehicle routing problem which accounts for nonholonomic constraints and dense, dynamic obstacles, called MVRP-DDO. The problem is strongly motivated by an industrial mining application. This paper illustrates how MVRP-DDO relates to other extensions of the vehicle routing problem. We provide an application-independent formulation of MVRP-DDO, as well as a concrete instantiation in a surface mining application. We propose a multi-abstraction search approach to compute an executable plan for the drilling operations of several machines in a very constrained environment. The approach is evaluated in terms of makespan and computation time, both of which are hard industrial requirements.Funding Agency:Atlas Copco Rock Drills ABSemantic Robot

Combined heuristic task and motion planning for bi-manual robots

Planning efficiently at task and motion levels allows the setting of new challenges for robotic manipulation problems, like for instance constrained table-top problems for bi-manual robots. In this scope, the appropriate combination of task and motion planning levels plays an important role. Accordingly, a heuristic-based task and motion planning approach is proposed, in which the computation of the heuristic addresses a geometrically relaxed problem, i.e., it only reasons upon objects placements, grasp poses, and inverse kinematics solutions. Motion paths are evaluated lazily, i.e., only after an action has been selected by the heuristic. This reduces the number of calls to the motion planner, while backtracking is reduced because the heuristic captures most of the geometric constraints. The approach has been validated in simulation and on a real robot, with different classes of table-top manipulation problems. Empirical comparison with recent approaches solving similar problems is also reported, showing that the proposed approach results in significant improvement both in terms of planing time and success rate.Peer Reviewe

Geometric backtracking for combined task and path planning in robotic systems

Planners for real, possibly complex, robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach in which state-based forward-chaining task planning is tightly coupled with sampling-based motion planning and other forms of geometric reasoning. We focus on the problem of geometric backtracking which arises when a planner needs to reconsider geometric choices, like grasps and poses, that were made for previous actions, in order to satisfy geometric preconditions of the current action. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the systematic exploration of the space of geometric states. In order to deal with that, we introduce heuristics based on the collisions between the robot and movable objects detected during geometric backtracking and on kinematic relations between actions. We also present a complementary approach based on propagating explicit constraints which are automatically generated from the symbolic actions to be evaluated and from the kinematic model of the robot. We empirically evaluate these dierent approaches. We demonstrate our planner on a real advanced robot, the DLR Justin robot, and on a simulated autonomous forklift. GeRTSAUN

Geometric backtracking for combined task and path planning in robotic systems

Planners for real, possibly complex, robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach in which state-based forward-chaining task planning is tightly coupled with sampling-based motion planning and other forms of geometric reasoning. We focus on the problem of geometric backtracking which arises when a planner needs to reconsider geometric choices, like grasps and poses, that were made for previous actions, in order to satisfy geometric preconditions of the current action. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the systematic exploration of the space of geometric states. In order to deal with that, we introduce heuristics based on the collisions between the robot and movable objects detected during geometric backtracking and on kinematic relations between actions. We also present a complementary approach based on propagating explicit constraints which are automatically generated from the symbolic actions to be evaluated and from the kinematic model of the robot. We empirically evaluate these dierent approaches. We demonstrate our planner on a real advanced robot, the DLR Justin robot, and on a simulated autonomous forklift. GeRTSAUN