Whole-Body Control (WBC) has emerged as an important framework in locomotion control for legged robots. However, most WBC frameworks fail to generalize beyond rigid terrains. Legged locomotion over soft terrain is difficult due to the presence of unmodeled contact dynamics that WBCs do not account for. This introduces uncertainty in locomotion and affects the stability and performance of the system. In this article, we propose a novel soft terrain adaptation algorithm called STANCE: Soft Terrain Adaptation and Compliance Estimation. STANCE consists of a WBC that exploits the knowledge of the terrain to generate an optimal solution that is contact consistent and an online terrain compliance estimator that provides the WBC with terrain knowledge. We validated STANCE both in simulation and experiment on the Hydraulically actuated Quadruped (HyQ) robot, and we compared it against the state-of-the-art WBC. We demonstrated the capabilities of STANCE with multiple terrains of different compliances, aggressive maneuvers, different forward velocities, and external disturbances. STANCE allowed HyQ to adapt online to terrains with different compliances (rigid and soft) without pretuning. HyQ was able to successfully deal with the transition between different terrains and showed the ability to differentiate between compliances under each foot.

We present a trajectory optimization framework for legged locomotion on rough terrain. We jointly optimize the center of mass motion and the foothold locations, while considering terrain conditions. We use a terrain costmap to quantify the desirability of a foothold location. We increase the gait's adaptability to the terrain by optimizing the step phase duration and modulating the trunk attitude, resulting in motions with guaranteed stability. We show that the combination of parametric models, stochastic-based exploration and receding horizon planning allows us to handle the many local minima associated with different terrain conditions and walking patterns. This combination delivers robust motion plans without the need for warm-starting. Moreover, we use soft-constraints to allow for increased flexibility when searching in the cost landscape of our problem. We showcase the performance of our trajectory optimization framework on multiple terrain conditions and validate our method in realistic simulation scenarios and experimental trials on a hydraulic, torque controlled quadruped robot.

Autonomous legged robots will be required to handle a wide range of tasks in complex environments. While a lot of research has focused on developing their abilities for periodic locomotion tasks, less effort has been invested in devising generalized strategies for dynamic, non-periodic movements. Motion design approaches are frequently enlisted in the form of teleoperation or predefined heuristics in such scenarios. We employ a realistic simulation of the hydraulically actuated HyQ2Max quadrupedal system for investigations on two distinctive tasks: rearing and posture recovery. We present a whole-body optimization methodology for non-periodic tasks on quadrupedal systems. This approach delivers solutions involving multiple contacts without the need for predefined feet placements. The results obtained show the potential of optimization approaches for motion synthesis in the context of complex tasks.

Genomes present various types of repeated structures having important roles in the mechanism of evolution. In particular, tandem repeats are analysed for their impact on genetic backgrounds of inherited diseases. However, the main objective of today's de novo assemblers is to output long, high-quality, assembled sequences; to this end, they use heuristic-based assembling procedures, which can leave many repeated regions unassembled - and in particular exact tandem repeats - due to the genomes complexity. In this paper, we propose an effective method, called DExTaR, that improves the detection of exact tandem repeats (ETRs) in any de novo de Bruijn assembly. DExTaR is based on a de Bruijn graph constructed by an assembler and retrieves ETRs left unassembled. When used with the well-known assembler ABySS, we show that DExTaR is able to obtain high quality results in terms of number and length of the detected ETRs.

We address the optimal control problem of robotic systems with variable stiffness actuation (VSA) including switching dynamics and discontinuous state transitions. Our focus in this paper is to consider tasks that have multiple phases of movement, contacts and impacts with the environment. By modelling such tasks as an approximate hybrid dynamical system with time-based switching, we develop a systematic methodology to simultaneously optimize control commands, stiffness profiles and temporal aspect of the movement such as switching instances and total movement duration. Numerical evaluations on a simple switching system, a realistic brachiating robot model with VSA, and a hopper with variable stiffness springs demonstrate the effectiveness of the proposed approach.