Concentric tube continuum robots (CTCRs) belong to the family of continuum robots with applications in minimally invasive surgeries. Because of this application domain, measuring the external forces along the body of the robot is paramount. CTCRs are made up of thin elastic rods and are intended to be applied inside the human body, where conventional sensor-based measurements are not feasible. Consequently, research is resorting to estimate the forces through geometric, numeric, or optimization methods. However, these methods often suffer from slow convergence. In this paper, we introduce a novel data-driven approach for estimating contact forces along the body of a CTCR that offers an estimation precision comparable to the current state-of-the-art optimization-based approaches, but exhibits nearly two orders of magnitude faster convergence. The proposed method is scalable and exhibits a significant performance in response to a wide range of external forces. The approach was evaluated in simulations and on a real 2-tube CTCR.

Numerous robotic and control applications have strict real-time requirements, which, when violated, result in reduced quality of service or, in case of safety critical applications, might even have catastrophic consequences. To ensure that certain real-time constraints are satisfied, roboticists have relied on real-time safe frameworks, environments and middleware. With the introduction of ROS 2, alongside kernel patches such as PREEMPT_RT, there is an abundance of solutions to pick from. This paper compares OROCOS and ROS 2 over PREEMPT_RT and vanilla Linux kernels in a variety of benchmarks and draws conclusions on their performance in real-time critical applications. The outcome of the benchmark shows comparable performances under normal conditions. However, when the system is under stress both frameworks suffer in different fashions. Furthermore, the results show an accumulating error which over time violates the real-time requirements in both frameworks. These findings are paramount in conducting real world application with real-time constraints.

In this paper, we look at the challenge of human robot interaction in locomotion. We consider a hand-in-hand interaction scenario where a human compliantly interacts with the upper-body of an impedance controlled humanoid. By exploring the velocity transmission of the robot arms, and the interaction in terms of robot arms manipulation quality evaluated through the monitoring of their manipulability the proposed method derives suitable reactive steps in appropriate directions to ensure that the robot manipulation ability is maintained with the robot arms providing high capacity of motion along the different directions. The proposed approach can be combined with different walking pattern generators and is not tailored to a specific one used in this work. The results of the proposed method are experimentally validated on the COMAN + humanoid robot showing the efficacy of the method to generate reactive stepping driven by the interaction and manipulation motion of the human operator. Besides, the work also provides a real-time software architecture to control humanoid COMAN+, but it is also flexible to be used for the control of other robot platforms.

We explore the characteristics of secondary contacts when applying forces with the end-effector of a robot and address the question when these secondary contacts can increase maximum applicable end-effector forces or reduce required actuator efforts. To this end, we formalize the effect of such secondary contacts in terms of required actuator efforts and derive efficiency bounds depending on the contact characteristics and robot configuration. Our findings are confirmed by experiments with a redundant serial manipulator.

Motor rehabilitation is in increasingly high demand to deal with minor functional motor impairments resulting from stroke, cerebellar ataxia, or Parkinson's disease. Juggling physiotherapy has shown to induce brain plasticity and to improve coordination and balance in this context. The physiotherapy, however, relies on large number of repetitions to be effective which prompts to deploy robots to release the burden on therapists both in terms of time as well as physical strain. This paper provides a framework to enable juggling games for patients in interacting with robots through Virtual Reality (VR). A set of throwing motions is recorded from the therapist and is retargeted to the humanoid robot COMAN's wrist. The respective whole-body motion is then solved in a stack of Quadratic Programs (QP) in a real-time architecture that integrates OROCOS and Gazebo. The resulting motion is finally streamed to VR for animation of the robot and the thrown ball, which the user can catch in VR using a controller device. We regard the VR setting as an essential step towards physiotherapeutic robotic juggling, because it ensures safety of the patients and effective testing of the methods and already has potential for actual therapeutic intervention. The control framework, however, is already validated in this paper for switching to full real-time operation on the physical robot.

This paper considers the problem of planning the motion of a humanoid robot that must execute a manipulation task, possibly requiring stepping, in environments cluttered by obstacles. The proposed method explores the submanifold of the configuration space that is admissible with respect to the assigned task and at the same time satisfies other constraints, including humanoid equilibrium. The exploration tree is expanded using a hybrid scheme that simultaneously generates footsteps and whole-body motions. The algorithm has been implemented for the humanoid robot NAO and validated through planning experiments and dynamic playback in V-REP.