Whole Body Affordances For Humanoid Robots A Computational Approach

The goal of this work is the development of a novel computational formalization of whole-body affordances which is suitable for the multimodal detection and validation of interaction possibilities in unknown environments. The hierarchical framework allows the consistent fusion of affordance-related evidence and can be utilized for realizing shared autonomous control of humanoid robots. The affordance formalization is evaluated in several experiments in simulation and on real humanoid robots.

The goal of this work is the development of a novel computational formalization of whole-body affordances which is suitable for the multimodal detection and validation of interaction possibilities in unknown environments. The hierarchical framework allows the consistent fusion of affordance-related evidence and can be utilized for realizing shared autonomous control of humanoid robots. The affordance formalization is evaluated in several experiments in simulation and on real humanoid robots. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

This book offers a comprehensive reference guide for modeling humanoid robots using intelligent and fuzzy systems. It provides readers with the necessary intelligent and fuzzy tools for controlling humanoid robots by incomplete, vague, and imprecise information or insufficient data, where classical modeling approaches cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including fuzzy control, metaheuristic-based control, neutrosophic control, etc. To foster reader comprehension, all chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers, and postgraduate students pursuing research on humanoid robots. Moreover, by extending all the main aspects of humanoid robots to its intelligent and fuzzy counterparts, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas, and developments.

Robustly maintaining balance on two legs is an important challenge for humanoid robots. The work presented in this book represents a contribution to this area. It investigates efficient methods for the decision-making from internal sensors about whether and where to step, several improvements to efficient whole-body postural balancing methods, and proposes and evaluates a novel method for efficient recovery step generation, leveraging human examples and simulation-based reinforcement learning.

Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot's view in order to explore interaction possibilities of the scene.

This book aims at providing algorithms for balance control of legged, torque-controlled humanoid robots. A humanoid robot normally uses the feet for locomotion. This paradigm is extended by addressing the challenge of multi-contact balancing, which allows a humanoid robot to exploit an arbitrary number of contacts for support. Using multiple contacts increases the size of the support polygon, which in turn leads to an increased robustness of the stance and to an increased kinematic workspace of the robot. Both are important features for facilitating a transition of humanoid robots from research laboratories to real-world applications, where they are confronted with multiple challenging scenarios, such as climbing stairs and ladders, traversing debris, handling heavy loads, or working in confined spaces. The distribution of forces and torques among the multiple contacts is a challenging aspect of the problem, which arises from the closed kinematic chain given by the robot and its environment.

Introducing mobile humanoid robots into human environments requires the systems to physically interact and execute multiple concurrent tasks. The monograph at hand presents a whole-body torque controller for dexterous and safe robotic manipulation. This control approach enables a mobile humanoid robot to simultaneously meet several control objectives with different pre-defined levels of priority, while providing the skills for compliant physical contacts with humans and the environment. After a general introduction into the topic of whole-body control, several essential reactive tasks are developed to extend the repertoire of robotic control objectives. Additionally, the classical Cartesian impedance is extended to the case of mobile robots. All of these tasks are then combined and integrated into an overall, priority-based control law. Besides the experimental validation of the approach, the formal proof of asymptotic stability for this hierarchical controller is presented. By interconnecting the whole-body controller with an artificial intelligence, the immense potential of the integrated approach for complex real-world applications is shown. Several typical household chores, such as autonomously wiping a window or sweeping the floor with a broom, are successfully performed on the mobile humanoid robot Rollin’ Justin of the German Aerospace Center (DLR). The results suggest the presented controller for a large variety of fields of application such as service robotics, human-robot cooperation in industry, telepresence in medical applications, space robotics scenarios, and the operation of mobile robots in dangerous and hazardous environments.

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Computational Human-Robot Interaction provides the reader with a systematic overview of the field of Human-Robot Interaction over the past decade, with a focus on the computational frameworks, algorithms, techniques, and models currently used to enable robots to interact with humans.