Purposeful interaction with objects usually requires certain constraints to be respected, e.g. keeping a bottle upright to avoid spilling. In reinforcement learning, such constraints are typically encoded in the reward function. As a consequence, constraints can only be learned by violating them. This often precludes learning on the physical robot, as it may take many trials to learn the constraints, and the necessity to violate them during the trial-and-error learning may be unsafe. We have serendipitously discovered that constraint representations for shared control - in particular Shared Control Templates (SCTs) - are ideally suited for safely guiding RL. Representing constraints explicitly, rather than implicitly in the reward function, also simplifies the design of the reward function. The main advantage of the approach is safer, faster learning without constraint violations (even with sparse reward functions). We demonstrate this in a pouring task in simulation and on a real robot, where learning the task requires only 65 episodes in 16 minutes.

Virtual Fixtures facilitate teleoperation, for in-stance by guiding the human operator. Developing these Virtual Fixtures in tasks with tight tolerances remains challenging. Fixtures with a high stiffness allow for more precise guidance, whereas a lower stiffness is required to allow for corrections. We observed that many assembly operations can be split into different phases - approaching, positioning, in-contact manipulation - each with different accuracy requirements. Therefore, we propose to use multi-modal fixtures, satisfying the different requirements of these phases: i.e. a position-based Trajectory Fixture for approaching and a more accurate Visual Servoing Fixture for the positioning phase. A state estimation and arbitration component ensures smooth transitions between the fixtures to provide optimal support for the operator and to achieve global availability paired with local precision at the same time. It also allows a high stiffness to be used throughout, thus achieving good guidance for all phases. The approach is validated in an application from a space scenario, consisting of the assembly of a CubeSat subsystem. The empirical results from a pilot study on this task show that our approach is faster and requires less interaction force from the operator than the baseline method.

In tendon-driven robotic hands, tendons are usually routed along several pulleys. The resulting friction is often substantial, and must therefore be modelled and estimated, for instance for accurate control and contact detection. Common approaches for friction estimation consider special dedicated setups, where the parameters of a static or dynamic friction model at a single contact point are determined. In this paper, we rather combine such individual friction models into an overall friction model for the entire finger. Furthermore, we propose a method for estimating the parameters of this overall model in situ, i.e. from trajectories executed on the assembled hand, avoiding the need for dedicated setups. An important component of the proposed model is a varying bias for treating friction at low velocities, allowing a simpler static friction model to be used. We demonstrate that our approach enables contacts to be detected more accurately on the DLR David hand, without additional sensors.

Controlling a robotic arm to achieve manipulation tasks is challenging for humans. Especially if only low-dimensional input signals can be provided, as is often the case for users with motor impairments. Using shared control to provide task-specific guidance and constraints facilitates control – for instance with the Shared Control Templates (SCT) framework – and enables even complex activities of daily living to be performed successfully. However, designing SCTs is a laborious task requiring robotic expertise. To make such design easier and faster, we propose a method for semi-automatically designing SCTs on the basis of demonstrations. Furthermore, we propose two similarity metrics, and demonstrate how these can be used to transfer knowledge from one SCT to another. We demonstrate that the SCTs so acquired can be successfully used in shared control for everyday tasks such as opening a drawer or a cupboard on our assistive robot EDAN.

Technology has sufficiently matured to enable, in principle, flexible and autonomous robotic assembly systems. However, in practice, it requires making all the relevant (implicit) knowledge that system engineers and workers have – about products to be assembled, tasks to be performed, as well as robots and their skills – available to the system explicitly. Only then can the planning and execution components of a robotic assembly pipeline communicate with each other in the same language and solve tasks autonomously without human intervention. This is why we have developed the Factory of the Future (FoF) ontology. At its core, this ontology models the tasks that are necessary to assemble a product and the robotic skills that can be employed to complete said tasks. The FoF ontology is based on existing standards. We started with theoretical considerations and iteratively adapted it based on practical experience gained from incorporating more and more components required for automated planning and assembly. Furthermore, we propose tools to extend the ontology for specific scenarios with knowledge about parts, robots, tools, and skills from various sources. The resulting scenario ontology serves us as world model for the robotic systems and other components of the assembly process. A central runtime interface to this world model provides fast and easy access to the knowledge during execution. In this work, we also show the integration of a graphical user front-end, an assembly planner, a workspace reconfigurator, and more components of the assembly pipeline that all communicate with the help of the FoF ontology. Overall, our integration of the FoF ontology with the other components of a robotic assembly pipeline shows that using an ontology is a practical method to establish a common language and understanding between the involved components.

Robotic tasks are becoming increasingly complex, and with this also the robotic systems. This requires new tools to manage this complexity and to orchestrate the systems to fulfill demanding autonomous tasks. For this purpose, we developed a new graphical tool targeting at the creation and execution of robotic tasks, called RAFCON. These tasks are described in hierarchical state machines supporting concurrency. A formal notation of this concept is given. The tool provides many debugging mechanisms and a GUI with a graphical editor, allowing for intuitive visual programming and fast prototyping. The application of RAFCON for an autonomous mobile robot in the SpaceBotCamp competition has already proved to be successful.

We present a (generalizable) method aimed to simultaneously transfer positional and force requirements encoded in a physical human skill (wood planing) from a human instructor to a robotic arm through kinesthetic teaching. We achieve our goal through a novel use of a common sensory configuration, constituted by a force/torque sensor mounted between the tool and the flange of a robotic arm. The robotic arm is endowed with integrated torque sensors at each joint. The mathematical model used to capture the general dynamic of the interaction between the human user and the wood surface is based on Dynamic Movement Primitives. During reenactment of the task, the system can imitate and generalize the demonstrated spatial requirements, as well as their associated force profiles. Therefore, the robotic arm acquires the capacity to reproduce the dynamic profile for in-contact tasks requiring an articulated coordination in the distribution of forces. For example, the capacity to effectively operate the plane on a wood plank over multiple strokes, according to the demonstration of the human instructor.