In this work we adapt and evaluate different solutions for automatic speech recognition (ASR) to be used as an HMI for the assistant robot. Two on-device solutions: Kaldi (DNN-HMM) and Mozilla's DeepSpeech (end-to-end), and three internet service APIs: IBM Watson, Microsoft Azure and Google Speech to Text are evaluated. The systems are adapted to the domain of robot commands and evaluated on a set of expected inputs. As the goal is to retain the ability to recognise general language, the systems are also evaluated on out of domain data.

In human-robot interaction, it is essential to ensure that the robot poses no threat to the human. Especially in applications that require close or physical interaction, e.g., collaborative manufacturing or rehabilitation, the danger emanating from the robot has to be minimized. Control schemes introducing virtual constraints have proven valuable in this context since they allow us to define a safe zone to move in without endangering the human. Combining the different requirements on the control scheme, such as real-time capability, stability, and reliability, in the presence of external disturbances and dynamic limits, however, turns out to be challenging. In this paper, we present a novel control scheme for human-robot interaction, which enforces dynamic constraints even in the presence of external forces. Based on an analytic constraint description and a feedback linearization of the system dynamics, a safe set of states is determined, which is then rendered controlled positively invariant, thus keeping the system in a safe configuration. The controlled system is analyzed with respect to invariance and boundedness with the results being illustrated in a full-scale experiment.

Many robotic applications, especially if humans are involved, require the robot to adhere to certain joint, workspace, velocity or force limits while simultaneously executing a task. In this paper, we introduce a control structure, which merges an arbitrary desired robot behavior with given constraints. Using a quadratic program (QP), control barrier functions (CBFs) are combined with an arbitrary nominal control law, which determines the desired behavior. The CBFs enforce the constraints, overruling nominal control whenever necessary. We show that the concept is applicable with arbitrary numbers of constraints and any nominal control law. In order to illustrate the capabilities of the approach, the control scheme is applied to an anthropomorphic manipulator, which is constrained by static as well as moving constraints.

In human-robot interaction (HRI) and especially in close or physical interaction, it is essential to ensure the human's safety. This is achieved by introducing virtual constraints defining a region, in which the robot is allowed to move safely. These safety regions may change over time during human-robot interaction, which may be either due to human motion or changed environmental conditions. In consequence it is important for the applied control scheme to handle dynamic boundaries. This work proposes an invariance-based control approach, which enforces adherence to boundaries with dynamic parameters. We extend the invariance control approach, which provides a computationally efficient and systematic method for defining constraints on system states and outputs, such that it handles the constraint dynamics. Stability and invariance properties are analyzed and validated in an experimental evaluation on a 7-DoF anthropomorphic manipulator.

The workspace and performance of a humanoid robot is decisively influenced by the design of its torso. The joints or spinal discs are usually the weak points due to the high stress they are exposed to, e. g. when lifting heavy objects. One way to circumvent the necessity of large motors is to use parallel mechanisms to optimize the distribution of loads. Here, we analyze the workspace of the humanoid robot Rollin' Justin of the German Aerospace Center (DLR) w. r. t. the constraints imposed by kinematic coupling of torso joints via tendons. The results of the analysis can be used for planning and reactive control to efficiently exploit the torso performance capabilities of the robotic system. As an application, we design a potential field based controller to avoid violating these constraints and implement it on the real robot.

Trajectory generation for active physical assistance to humans in cooperative haptic tasks gains increasing interest in recent literature. Planning-based approaches represent one class of trajectory synthesis methods for active robotic partners. To overcome the limitations of kinematic planning algorithms in dynamic tasks, we propose a three-step approach to the synthesis of trajectories under the principle of least action. This is motivated by neuroscientific findings on human effort minimization in motor tasks. A trajectory is generated by optimized sequencing of optimal motion primitives. The benefits of the proposed method for physical human-robot cooperation are demonstrated in human user studies in a 2D cooperative transport task in a virtual maze.

For safety in physical human-robot interaction (pHRI) the robot motion must be restricted to an admissible (safe) region. In this work, we propose a systematic approach to guarantee the satisfaction of virtual workspace constraints in 6D for arbitrary manipulator dynamics based on an extended invariance control concept. Invariance control yields a computationally efficient method to render multiple virtual nonlinear workspace boundaries. In order to make the scheme suitable for pHRI we present an approach to reduce chattering by explicitly considering the discrete-time Euler solver output. Orientation constraints are unambiguously represented as unit quaternions. The theoretical results are successfully validated in simulation and experiments on a 7-DoF anthropomorphic manipulator.