Borinot: an agile torque-controlled robot for hybrid flying and contact loco-manipulation

This paper introduces Borinot, an open-source flying robotic platform designed to perform hybrid agile locomotion and manipulation. This platform features a compact and powerful hexarotor that can be outfitted with torque-actuated extremities of diverse architecture, allowing for whole-body dynamic control. As a result, Borinot can perform agile tasks such as aggressive or acrobatic maneuvers with the participation of the whole-body dynamics. The extremities attached to Borinot can be utilized in various ways; during contact, they can be used as legs to create contact-based locomotion, or as arms to manipulate objects. In free flight, they can be used as tails to contribute to dynamics, mimicking the movements of many animals. This allows for any hybridization of these dynamic modes, like the jump-flight of chicken and locusts, making Borinot an ideal open-source platform for research on hybrid aerial-contact agile motion. To demonstrate the key capabilities of Borinot, we have fitted a planar 2DoF arm and implemented whole-body torque-level model-predictive-control. The result is a capable and adaptable platform that, we believe, opens up new avenues of research in the field of agile robotics.

WOLF: A modular estimation framework for robotics based on factor graphs

This paper introduces WOLF, a C++ estimation framework based on factor graphs and targeted at mobile robotics. WOLF extends the applications of factor graphs from the typical problems of SLAM and odometry to a general estimation framework able to handle self-calibration, model identification, or the observation of dynamic quantities other than localization. WOLF produces high throughput estimates at sensor rates up to the kHz range, which can be used for feedback control of highly dynamic robots such as humanoids, quadrupeds or aerial manipulators. Departing from the factor graph paradigm, the architecture of WOLF allows for a modular yet tightly-coupled estimator. Modularity is based on plugins that are loaded at runtime. Then, integration is achieved simply through YAML files, allowing users to configure a wide range of applications without the need of writing or compiling code. Synchronization of incoming data and their processing into a unique factor graph is achieved through a decentralized strategy of frame creation and joining. Most algorithmic assets are coded as abstract algorithms in base classes with varying levels of specialization. Overall, these assets allow for coherent processing and favor code reusability and scalability. WOLF can be interfaced with different solvers, and we provide a wrapper to Google Ceres. Likewise, we offer ROS integration, providing a generic ROS node and specialized packages with subscribers and publishers. WOLF is made publicly available and open to collaboration.

A Direct-Indirect Hybridization Approach to Control-Limited DDP

Optimal control is a widely used tool for synthesizing motions and controls for user-defined tasks under physical constraints. A common approach is to formulate it using direct multiple-shooting and then to use off-the-shelf nonlinear programming solvers that can easily handle arbitrary constraints on the controls and states. However, these methods are not fast enough for many robotics applications such as real-time humanoid motor control. Exploiting the sparse structure of optimal control problem, such as in Differential DynamicProgramming (DDP), has proven to significantly boost the computational efficiency, and recent works have been focused on handling arbitrary constraints. Despite that, DDP has been associated with poor numerical convergence, particularly when considering long time horizons. One of the main reasons is due to system instabilities and poor warm-starting (only controls). This paper presents control-limited Feasibility-driven DDP (Box-FDDP), a solver that incorporates a direct-indirect hybridization of the control-limited DDP algorithm. Concretely, the forward and backward passes handle feasibility and control limits. We showcase the impact and importance of our method on a set of challenging optimal control problems against the Box-DDP and squashing-function approach.