Cognitive Reasoning for Compliant Robot Manipulation

Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots

Robotic Deployment of Extraterrestrial Seismic Networks

Manual installation of seismic networks in extraterrestrial environments is risky, expensive and error-prone. A more reliable alternative is the automated deposition with a light-weight robot manipulator. However, inserting a spiked sensor into soil is a challenging task for a robot since the soil parameters are variable and difficult to estimate. Therefore, we investigate an approach to accurate insertion and positioning of geophones using a Cartesian impedance controller with a feed-forward force term. The feed-forward force component of the controller is either estimated using the Fundamental Earth-Moving Equation, the Discrete Element Method or empirically. For the first time, both the geological aspects of the problem as well as the aspects of robotic control are considered. Based on this consideration, the control approach is enhanced by predicting the resistance force of the soil. Experiments with the humanoid robot Rollin’ Justin inserting a geophone into three different soil samples validate the proposed method

Context-aware Mission Control for Astronaut-Robot Collaboration

Space robot assistants are envisaged as semi-autonomous co-workers deployed to lighten the workload of astronauts in cumbersome and dangerous situations. In view of this, this work considers the prospects on the technology requirements for future space robot operations, by presenting a novel mission control concept for close astronaut-robot collaboration. A decentralized approach is proposed, in which an astronaut is put in charge of commanding the robot, and a mission control center on Earth maintains a list of authorized robot actions by applying symbolic, geometric, and context-specific filters. The concept is applied to actual space robot operations within the METERON SUPVIS Justin experiment. In particular, it is shown how the concept is utilized to guide an astronaut aboard the ISS in its mission to survey and maintain a solar panel farm in a simulated Mars environment

Classifying Compliant Manipulation Tasks for Automated Planning in Robotics

Many household chores and industrial manufacturing tasks require a certain compliant behavior to make deliberate physical contact with the environment. This compliant behavior can be implemented by modern robotic manipulators. However, in order to plan the task execution, a robot requires generic process models of these tasks which can be adapted to different domains and varying environmental conditions. In this work we propose a classification of compliant manipulation tasks meeting these requirements, to derive related actions for automated planning. We also present a classification for the sub-category of wiping tasks, which are most common and of great importance in service robotics. We categorize actions from an object-centric perspective to make them independent of any specific robot kinematics. The aim of the proposed taxonomy is to guide robotic programmers to develop generic actions for any kind of robotic systems in arbitrary domains

Intergroup Conflict Self-Perpetuates via Meaning: Exposure to Intergroup Conflict Increases Meaning and Fuels a Desire for Further Conflict

We investigated whether violent conflict provides individuals with a sense of meaning that they are hesitant to let go of, thus contributing to the perpetuation of intergroup conflict. Across a wide variety of contexts, we found that making intergroup conflict salient increased the meaning people found in conflict and, in turn, increased support for conflict-perpetuating beliefs, ideologies, policies, and behaviors. These effects were detected among participants exposed to reminders of intergroup conflict (the American Revolutionary War and the U.S.-led campaign against ISIS; Studies 1A and 1B), participants living through actual intergroup conflict (the 2014 Israel-Gaza war; Study 2), and participants who perceived actual intergroup conflicts to be larger versus smaller in scope (the November 2015 Paris attacks; Studies 3 and 4). We also found that directly manipulating the perceived meaning in conflict (in the context of the 2014 NYC "hatchet attack"; Study 5) led to greater perceived meaning in life in general and thereby greater support for conflict escalation. Together, these findings suggest that intergroup conflict can serve as a source of meaning that people are motivated to hold on to. We discuss our findings in the context of the meaning making and threat compensation literatures, and consider their implications for perspectives on conflict escalation and resolution

Multi-Agent Heterogeneous Digital Twin Framework with Dynamic Responsibility Allocation for Complex Task Simulation

To become helpful assistants in our daily lives, robots must be able to understand the effects of their actions on their environment. A modern approach to this is the use of a physics simulation, where often very general simulation engines are utilized. As a result, specific modeling features, such as multi-contact simulation or fluid dynamics, may not be well represented. To improve the representativeness of simulations, we propose a framework for combining estimations of multiple heterogeneous simulations into a single one. The framework couples multiple simulations and reorganizes them based on semantically annotated action sequence information. While each object in the scene is always covered by a simulation, this simulation responsibility can be reassigned on-line. In this paper, we introduce the concept of the framework, describe the architecture, and demonstrate two example implementations. Eventually, we demonstrate how the framework can be used to simulate action executions on the humanoid robot Rollin' Justin with the goal to extract the semantic state and how this information is used to assess whether an action sequence is executed successful or not

Probabilistic Effect Prediction through Semantic Augmentation and Physical Simulation

Nowadays, robots are mechanically able to perform highly demanding tasks, where AI-based planning methods are used to schedule a sequence of actions that result in the desired effect. However, it is not always possible to know the exact outcome of an action in advance, as failure situations may occur at any time. To enhance failure tolerance, we propose to predict the effects of robot actions by augmenting collected experience with semantic knowledge and leveraging realistic physics simulations. That is, we consider semantic similarity of actions in order to predict outcome probabilities for previously unknown tasks. Furthermore, physical simulation is used to gather simulated experience that makes the approach robust even in extreme cases. We show how this concept is used to predict action success probabilities and how this information can be exploited throughout future planning trials. The concept is evaluated in a series of real world experiments conducted with the humanoid robot Rollin’ Justin

Addressing the Entry Barrier for Experimentation in Perception-aware Trajectory Planning for Planetary Rovers

To fully evaluate perception-aware planning methods for planetary rover, we need a platform that takes action commands and captures perception data. Even with suitable hardware and simulation available to us, there exists an "entry barrier" for performing research in active vision, as developing methods with a system-in-the-loop is time intensive. We present our approach for tackling this entry barrier by incrementally moving from toy examples to integration and deployment on real robots. Our approach aims at reducing the overall development complexity by producing intermediate results that are used to validate and evaluate the active algorithm

CATs: Task Planning for Shared Control of Assistive Robots with Variable Autonomy

Abstract: From robotic space assistance to healthcare robotics, there is increasing interest in robots that offer adaptable levels of autonomy. In this paper, we propose an action representation and planning framework that is able to generate plans that can be executed with both shared control and supervised autonomy, even switching between them during task execution. The action representation -- Constraint Action Templates (CATs) -- combine the advantages of Action Templates (Leidner, 2019) and Shared Control Templates (Quere, 2020). We demonstrate that CATs enable our planning framework to generate goal-directed plans for variations of a typical task of daily living, and that users can execute them on the wheelchair-robot EDAN in shared control or in autonomous mode