In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automated recovery strategies, while effective for predefined scenarios, often lack the flexibility required for on-the-fly task management and adaptation to expected failures. Addressing this gap, we propose a novel approach that models recovery behaviors as adaptable robotic skills, leveraging the Behavior<br>Trees and Motion Generators (BTMG) framework for policy representation. This approach distinguishes itself by employing reinforcement learning (RL) to dynamically refine recovery behavior parameters, enabling a tailored response to a wide array of failure scenarios with minimal human intervention.<br>We assess our methodology through a series of progressively challenging scenarios within a peg-in-a-hole task, demonstrating the approach’s effectiveness in enhancing operational efficiency<br>and task success rates in collaborative robotics settings. We validate our approach using a dual-arm KUKA robot.<br>

This paper describes a novel approach to adaptive manufacturing in the context of small batch production and customization. It focuses on integrating task-level planning and reasoning with reinforcement learning (RL) in the SkiROS2 skill-based robot control platform. This integration enhances the efficiency and adaptability of robotic systems in manufacturing, enabling them to adjust to task variations and learn from interaction data. The paper highlights the architecture of SkiROS2, particularly its world model, skill libraries, and task management. It demonstrates how combining RL with robotic manipulators can learn and improve the execution of industrial tasks. It advocates a multi-objective learning model that eases the learning problem design. The approach can incorporate user priors or previous experiences to accelerate learning and increase safety.

The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a specific task may not be robust to the changes in the environment and may not generalize well to different variations of tasks. We propose to extend the BTMG policy representation with a module that predicts BTMG parameters for a new task variation. To achieve this, we propose a model that combines a Gaussian process and a weighted support vector machine classifier. This model predicts the performance measure and the feasibility of the predicted policy with BTMG parameters and task variations as inputs. Using the outputs of the model, we then construct a surrogate reward function that is utilized within an optimizer to maximize the performance of a task over BTMG parameters for a fixed task variation. To demonstrate the effectiveness of our proposed approach, we conducted experimental evaluations on push and obstacle avoidance tasks in simulation and with a real KUKA iiwa robot. Furthermore, we compared the performance of our approach with four baseline methods.

The transition to agile manufacturing, Industry 4.0, and high-mix-low-volume tasks require robot programming solutions that are flexible. However, most deployed robot solutions are still statically programmed and use stiff position control, which limit their usefulness.<br>In this paper, we show how a single robot skill that utilizes knowledge representation, task planning, and automatic selection of skill implementations based on the input parameters can be executed in different contexts. We demonstrate how the skill-based control platform enables this with contactrich wiping tasks on different robot systems. To achieve that in this case study, our approach needs to address different<br>kinematics, gripper types, vendors, and fundamentally different control interfaces. We conducted the experiments with a mobile platform that has a Universal Robots UR5e 6 degree-of-freedom robot arm with position control and a 7 degree-of-freedom KUKA iiwa with torque control.

We study construction problems where multiple robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by a surface or a robot and stability of the overall structure at all times. We propose a general elaboration tolerant method to solve a wide range of construction problems, based on the knowledge representation and reasoning paradigm of Answer Set Programming. This method not only (i) determines a stable final configuration of the structure, but also (ii) computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, (iii) while simultaneously ensuring the requirements of supportedness and stability at all times. We prove the soundness and completeness of our method with respect to these properties. We introduce a set of challenging construction benchmark instances, including construction of (uneven) bridges and overhangs, and discuss the usefulness of our framework over these instances. Furthermore, we perform experiments to investigate the computational performance of our hybrid method, and demonstrate the applicability of our method using a bimanual Baxter robot.

One of today's goals for industrial robot systems is to allow fast and easy provisioning for new tasks. Skill-based systems that use planning and knowledge representation have long been one possible answer to this. However, especially with contact-rich robot tasks that need careful parameter settings, such reasoning techniques can fall short if the required knowledge not adequately modeled. We show an approach that provides a combination of task-level planning and reasoning with targeted learning of skill parameters for a task at hand. Starting from a task goal formulated in PDDL, the learnable parameters in the plan are identified and an operator can choose reward functions and parameters for the learning process. A tight integration with a knowledge framework allows to form a prior for learning and the usage of multi-objective Bayesian optimization eases to balance aspects such as safety and task performance that can often affect each other. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks and show their successful execution on a real 7-DOF KUKA-iiwa.

In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: the user provides a task goal in the planning language PDDL, then a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified, and, finally, an operator chooses reward functions and hyperparameters for the learning process. Two aspects of our methodology are critical: (a) learning is tightly integrated with a knowledge framework to support symbolic planning and to provide priors for learning, (b) using multi-objective optimization. This can help to balance key performance indicators<br>(KPIs) such as safety and task performance since they can<br>often affect each other. We adopt a multi-objective Bayesian<br>optimization approach and learn entirely in simulation. We<br>demonstrate the efficacy and versatility of our approach by<br>learning skill parameters for two different contact-rich tasks.<br>We show their successful execution on a real 7-DOF KUKA-iiwa<br>manipulator and outperform the manual parameterization by<br>human robot operators.

We propose a generalisation of a behaviour tree and motiongenerator based robot arm policy representation for learning<br>and solving tasks such as contact-rich tasks like peg insertion<br>or pushing an object. We use planning to generate skill sequences needed to execute these tasks and rely on reinforcement learning to obtain parameters of the policy. We assume<br>gaussian processes as a suitable method for this generalisation and present preliminary, promising results from initial<br>experiments.

Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL algorithms still require thousands of trials to converge to high-performing policies, can produce dangerous behaviors while learning, and the optimized policies (usually modeled as neural networks) give almost zero explanation when they fail to perform the task. For these reasons, the adoption of RL in industrial settings is not common. Behavior Trees (BTs), on the other hand, can provide a policy representation that a) supports modular and composable skills, b) allows for easy interpretation of the robot actions, and c) provides an advantageous low-dimensional parameter space. In this paper, we present a novel algorithm that can learn the parameters of a BT policy in simulation and then generalize to the physical robot without any additional training. We leverage a physical simulator with a digital twin of our workstation, and optimize the relevant parameters with a black-box optimizer. We showcase the efficacy of our method with a 7-DOF KUKAiiwa manipulator in a task that includes obstacle avoidance and a contact-rich insertion (peg-in-hole), in which our method outperforms the baselines.

We study robot construction problems where multiple autonomous robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by other blocks and stability of the structure at all times. We propose a formal hybrid planning framework to solve a wide range of robot construction problems, based on Answer Set Programming. This framework not only decides for a stable final configuration of the structure, but also computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, while simultaneously ensuring the stability, supportedness and other desired properties of the partial construction at each step of the plan. We prove the soundness and completeness of our formal method with respect to these properties. We introduce a set of challenging robot construction benchmark instances, including bridge building and stack overhanging scenarios, discuss the usefulness of our framework over these instances, and demonstrate the applicability of our method using a bimanual Baxter robot.

We study robot construction problems where multiple autonomous robots rearrange prefabricated components to build stable structures. Robot construction problems can play a vital role in construction industries where the tasks such as designing a desired structure, planning for the necessary actions, and constructing structures from available components can be performed by the robots. Robotic construction may especially be useful in places, such as disaster zones or the space, where it is not safe or feasible for humans to visit. In these unsafe or hard-to-reach places, robots can build necessary buildings, bridges or shelters using the surrounding materials.<br>We view robot construction problems as planning problems: find a plan (i.e., a sequence of actions) to obtain a final stable configuration of prefabricated objects satisfying some goal conditions, from a given initial configuration. These problems are challenging from the perspective of task planning since they may need incorporation of preexisting structure into the final design, pre-assembly of movable substructures, and use of extra blocks as temporary supports or counterweights during construction. These problems are challenging from the perspective of geometric reasoning as well, since they need feasibility checks to ensure reachability of a block, to avoid collisions of blocks, and to ensure stability of complex structures.<br>We propose a formal hybrid planning framework to address these challenges using Answer Set Programming, and state-of-the-art feasibility checkers. This framework not only decides for a stable final configuration of the<br>structure, but also computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, while simultaneously ensuring the stability, supportedness and other desired properties of the partial construction at each step of the plan.<br>We show the usefulness of our approach on a wide variety of robot construction tasks, including bridge building and overhang construction scenarios, and using different types of objects, including cylindrical ones.<br>We demonstrate the applicability of our approach through dynamic simulations and physical implementations with a bi-manual Baxter robot.

We consider the robot construction planning problems introduced by Scott Elliott Fahlman in his seminal article, where the aim is for a robot to build specified structures out of simple rectangular blocks of different sizes. These problems are challenging from the perspective of task planning since they need incorporation of preexisting structure into the final design, pre-assembly of movable substructures on the table, and the use of extra blocks as temporary supports or counterweights during construction. They are challenging from the perspective of geometric reasoning as well since they need feasibility checks, like reachability of a block, collisions of<br>blocks, and stability of complex structures.