Task and Motion Planning (TAMP) is a computationally challenging robotics problem due to the tight coupling of discrete symbolic planning and continuous geometric planning of robot motions. In particular, planning manipulation tasks in complex 3D environments leads to a large number of costly geometric planner queries to verify the feasibility of considered actions and plan their motions. To address this issue, we propose Geometric Reasoning Networks (GRN), a graph neural network (GNN)-based model for action and grasp feasibility prediction, designed to significantly reduce the dependency on the geometric planner. Moreover, we introduce two key interpretability mechanisms: inverse kinematics (IK) feasibility prediction and grasp obstruction (GO) estimation. These modules not only improve feasibility predictions accuracy, but also explain why certain actions or grasps are infeasible, thus allowing a more efficient search for a feasible solution. Through extensive experimental results, we show that our model outperforms state-of-the-art methods, while maintaining generalizability to more complex environments, diverse object shapes, multi-robot settings, and real-world robots.

Taking into account the effects of parameter uncertainties in the robot model is crucial to the robustness of motion generation. One approach to address this issue is to compute ‘uncertainty tubes’ enveloping the robot state for any combination of parameters within a given range, and to use these tubes to robustly check for collisions within a motion planning algorithm. However, computing these tubes for complex dynamical systems can be too computationally expensive due to the need to solve and integrate potentially numerous nonlinear ordinary differential equations (ODEs) associated with robot dynamics. To overcome this limitation, we propose a GRU-based architecture that provides fast and accurate estimation of these uncertainty tubes. We demonstrate that GRUs achieve the best compromise between prediction accuracy, prediction time, and network size compared to basic RNNs and LSTMs, justifying our choice. Finally, we showcase the efficiency of the learning process within a motion planning framework for an aerial vehicle.

The hybrid discrete/continuous nature of task and motion planning (TAMP) results often in a combinatorial explosion. This challenge is even more pronounced in multi-robot TAMP problems due to the increase in dimensionality of the action space. Previous works use action feasibility prediction as a heuristic to accelerate TAMP. However, these methods are limited to box-shaped objects and specific single or dual robot settings. In this paper, we propose a feasibility-enabled multi-robot TAMP algorithm capable of tackling complex multi-robot manipulation problems. Also, we expand on our previous work on action and grasp feasibility prediction [1] by extending its use to mesh-shaped objects. We demonstrate the performance of our method compared to a non feasibility-informed baseline, and show its ability to handle TAMP problems requiring the collaboration of multiple robots.

In Task and motion planning (TAMP), symbolic search is combined with continuous geometric planning. A task planner finds an action sequence while a motion planner checks its feasibility and plans the corresponding sequence of motions. However, due to the high combinatorial complexity of discrete search, the number of calls to the geometric planner can be very large. Previous works [1] [2] leverage learning methods to efficiently predict the feasibility of actions, much like humans do, on tabletop scenarios. This way, the time spent on motion planning can be greatly reduced. In this work, we generalize these methods to 3D environments, thus covering the whole workspace of the robot. We propose an efficient method for 3D scene representation, along with a deep neural network capable of predicting the probability of feasibility of an action. We develop a simple TAMP algorithm that integrates the trained classifier, and demonstrate the performance gain of using our approach on multiple problem domains. On complex problems, our method can reduce the time spent on geometric planning by up to 90%.

In this paper, we address task and motion plan-ning (TAMP) which is an important yet challenging robotics problem. It is known to suffer from the high combinatorial complexity of discrete search, often requiring a large number of geometric planning calls. We build upon recent works in TAMP by taking advantage of learning methods to provide action feasibility information as a heuristic to the symbolic planner, thus guiding it to a geometrically feasible solution and reducing geometric planning time. We propose AGFP-Net, a multi-task neural network predicting not only action feasibility, but also the feasibility of a set of grasp types. We also propose an improved feasibility-informed TAMP algorithm capable of solving more complex problems, and handling goals which are not fully specified. Comparative results obtained on different problems of varying complexity show that our method is able to greatly reduce task and motion planning time.