Integrating metric time into Task And Motion Planning (TAMP) is challenging, especially with simultaneous object motion. Existing work focuses on classical and numeric TAMP, not considering deadlines, motions overlapping in time, and other temporal constraints. In this paper, we fill this gap by formalizing Temporal Task and Motion Planning (TTAMP) for multi-object navigation. We propose a novel interleaved planning technique for this problem, which leverages incremental Satisfiability Modulo Theory to ensure efficient reasoning on deadlines and action duration coupled with a motion planner supporting simultaneous object motion. Geometric data on encountered obstacles prunes unreachable symbolic regions, while temporal bounds limit the geometric search space. For multiple moving objects, our algorithm contextualizes the conflicts learned from the motion planner on overlapping actions so that entire classes of temporal plans are pruned from the search space of the task planner, ensuring the eventual termination of the interplay. We provide a comprehensive benchmark suite and demonstrate the effectiveness of our solver in leveraging these scenarios.

Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem that includes discrete actions executable by low-level continuous motions. This field is gaining increasing interest within the robotics community as it significantly enhances robot’s autonomy in real-world applications. Many solutions and formulations exist, but no clear standard representation has emerged. In this paper, we propose a general and open-source framework for modeling and benchmarking TAMP problems. Moreover, we introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles. This approach enables using any off-the-shelf task planner and motion planner while leveraging a geometric analysis of the motion planner’s search space to prune the task planner’s exploration, enhancing its efficiency. We also show how to specialize this meta-engine for the case of an incremental SMT-based planner. We demonstrate the effectiveness of our approach across benchmark problems of increasing complexity, where robots must navigate environments with movable obstacles. Finally, we integrate state-of-the-art TAMP algorithms into our framework and compare their performance with our achievements.

In shared control teleoperation, the robot assists the user in accomplishing the desired task. Rather than simply executing the user’s command, the robot attempts to integrate it with information from the environment, such as obstacle and/or goal locations, and it modifies its behavior accordingly. In this article, we propose a real-time shared control teleoperation framework based on an artificial potential field approach improved by the dynamic generation of escape points around the obstacles. These escape points are virtual attractive points in the potential field that the robot can follow to overcome the obstacles more easily. The selection of which escape point to follow is done in real time by solving a soft-constrained problem optimizing the reaching of the most probable goal, estimated from the user’s action. Our proposal has been extensively compared with two state-of-the-art approaches in a static cluttered environment and a dynamic setup with randomly moving objects. Experimental results showed the efficacy of our method in terms of quantitative and qualitative metrics. For example, it significantly decreases the time to complete the tasks and the user’s intervention, and it helps reduce the failure rate. Moreover, we received positive feedback from the users that tested our proposal. Finally, the proposed framework is compatible with both mobile and manipulator robots.

This work proposes a general formulation for the Object Sorting problem, suitable to describe any non-deterministic environment characterized by friendly and adversarial interference. Such an approach, coupled with a Deep Reinforcement Learning algorithm, allows training policies to solve different sorting tasks without adjusting the architecture or modifying the learning method. Briefly, the environment is subdivided into a clutter, where objects are freely located, and a set of clusters, where objects should be placed according to predefined ordering and classification rules. A 3D grid discretizes such environment: the properties of an object within a cell depict its state. Such attributes include object category and order. A Markov Decision Process formulates the problem: at each time step, the state of the cells fully defines the environment's one. Users can custom-define object classes, ordering priorities, and failure rules. The latter by assigning a non-uniform risk probability to each cell. Performed experiments successfully trained and validated a Deep Reinforcement Learning model to solve several sorting tasks while minimizing the number of moves and failure probability. Obtained results demonstrate the capability of the system to handle non-deterministic events, like failures, and unpredictable external disturbances, like human user interventions.

This paper proposes a Deep Reinforcement Learning powered approach for tabletop object sorting. Once perceived the environment, the system creates a semantic representation of the scene, describing the pose and category of each recognized object. This image is then provided as input to the trained Deep Neural Network in charge of choosing the correct action to be performed to successfully achieve the sorting task. Obtained results prove the capability of the proposed system, including its intrinsic robustness to failures and unpredictable interactions with humans or other environmental agents. Moreover, the use of semantic images makes the Deep Neural Network independent from the type of objects to be sorted and from their final placement location. Finally, the system is scalable, being capable of sorting as many known objects as recognized by the perception system. Currently, the system can sort objects belonging to two predefined categories while treating all the others as obstacles. Future works will extend the system making it capable of sorting potentially any type and number of object categories.

The paper describes how the graduate course "Autonomous Robotics" innovatively introduces robotics to Master of Science students of the Faculty of Computer Engineering of the University of Padova (Italy). The main contributions are: 1) The adoption of a Project-Based Learning constructivist approach. This teaching methodology makes students able to autonomously build their robotic knowledge base; 2) The assignment of laboratory experiences according to an increasing difficulty, from mobile robots (the simple Lego Mindstorms NXT) to humanoids (the Vstone Robovie-X and the Aldebaran NAO). Humanoids are not a widespread teaching tool because of their complexity: the course simplifies the resolution of the robots stability problem by adopting teleoperation; 3) The adoption of the open-source Robot Operating System framework. The framework encourages students to implement reusable code. The effectiveness of the adopted approach has been proven building a team of students that had successfully concluded the course. The team is participating in the European Robotics Challenges and has successfully accomplished the challenge's first stage.