The rise of collaborative robotics has offered new opportunities for integrating automation into the factories, allowing robots and humans to work side-by-side. However, this close physical coexistence inevitably brings new constraints for ensuring safe human-robot cooperation. The current paramount challenge is integrating human safety constraints without compromising the robotic performance goals, which require minimization of the task execution time alongside ensuring its accomplishment. This letter proposes a novel robot trajectory planning algorithm to produce minimum-time yet safe motion plans along specified paths in shared workspaces with humans. To this end, a safety module was used to evaluate the safety of a time-optimal trajectory iteratively. A safe replanning module was developed to optimally adapt the generated trajectory online whenever the optimal plan violates dynamically provided safety limits. In order to preserve performance, a recovery trajectory planning algorithm was included such that the robot is allowed to restore higher speed motions as soon as the safety concern has been resolved. The proposed solution's effectiveness was evaluated both in simulations and real experiments with two robotic manipulators.

Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.

Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedures that will boost not only the scientific development of better bioinspired solutions, but also their market uptake.

The manufacturing industry is among the top wealth generating sectors of the global economy and accounted for 15.3% and 10%, respectively, of the total European and American workforce in 2018 [1], [2]. Despite its crucial role, manufacturing is facing a critical challenge based on a reduction of skilled labor availability. This trend is impos- ing a bottleneck on growth due to the demands of an increasingly competitive market. The aging workforce is not helping this shortfall either, as the available workforce is less able to perform burdensome industrial tasks in an efficient and productive manner.

The vast majority of state-of-the-art walking robots employ flat or ball feet for locomotion, presenting limitations while stepping on obstacles, slopes, or unstructured terrain. Moreover, traditional feet for quadrupeds lack sensing systems that are able to provide information about the environment and about the foot interaction with the surroundings. This further diminishes their value. Inspired by our previous work on soft feet for bipedal robots, we present the SoftFoot-Q, an articulated adaptive foot for quadrupeds. This device is conceived to be robust and able to overcome the limitations of currently employed feet. The core idea behind our adaptive foot design is first introduced and validated through a simplified mathematical formulation of the problem. Subsequently, we present the chosen mechanical implementation to attempt overcoming current limitations. The realized prototype of adaptive foot is integrated and tested on the compliantly actuated quadrupedal robot ANYmal together with an ROS-based real-time foot pose reconstruction software. Both extensive field tests and indoor experiments show noticeable performance improvements, in terms of reduced slippage of the robot, with respect to both flat and ball feet.

This work proposes a method to grasp unknown objects with robotic hands based on demonstrations by a skilled human operator. Not only are humans efficacious at grasping with their own hands but are also capable of grasping objects using robotic hands. Therefore, we consider how the grasping skills of a human trained in robotic hand use can be transferred to a robot equipped with the same hand. We propose that a skilled human user manually operates the robotic hand to grasp a number of elementary objects, consisting of different boxes. A Decision Tree Regressor is trained on the data acquired from the human operator to generate hand poses able to grasp a general box. This is extended to grasp objects of unknown and general shape leveraging upon the state-of-theart Minimum Volume Bounding Box decomposition algorithm that approximates with a number of boxes the point cloud of the object. We report on extensive tests of the proposed approach on a Panda manipulator equipped with a Pisa/IIT SoftHand, achieving a success rate of 86.7% over 105 grasps of 21 different objects.