The Pinocchio C++ library : A fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives

Carpentier, Justin; Saurel, Guilhem; Buondonno, Gabriele; Mirabel, Joseph; Lamiraux, Florent; Stasse, Olivier; Mansard, Nicolas

doi:10.1109/sii.2019.8700380

Cited by 232 publications

(172 citation statements)

References 28 publications

(35 reference statements)

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“…All these methods have in common that they solve the nonlinear Optimal Control (OC) problem by iteratively building and solving a Linear-Quadratic Regulator (LQR) problem. These frameworks use numerical or automatic differentiation which is inefficient compared to sparse and analytical derivatives [12]. Furthermore, they do not handle geometrical systems, typically found in legged systems as the floating-base is a SE(3) element.…”

Section: Introductionmentioning

confidence: 99%

Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control

Mastalli

Budhiraja

Merkt

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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213

226

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We introduce Crocoddyl (Contact RObot COntrol by Differential DYnamic Library), an open-source framework tailored for efficient multi-contact optimal control. Crocoddyl efficiently computes the state trajectory and the control policy for a given predefined sequence of contacts. Its efficiency is due to the use of sparse analytical derivatives, exploitation of the problem structure, and data sharing. It employs differential geometry to properly describe the state of any geometrical system, e.g. floating-base systems. We have unified dynamics, costs, and constraints into a single concept-action-for greater efficiency and easy prototyping. Additionally, we propose a novel multipleshooting method called Feasibility-prone Differential Dynamic Programming (FDDP). Our novel method shows a greater globalization strategy compared to classical Differential Dynamic Programming (DDP) algorithms, and it has similar numerical behavior to state-of-the-art multiple-shooting methods. However, our method does not increase the computational complexity typically encountered by adding extra variables to describe the gaps in the dynamics. Concretely, we propose two modifications to the classical DDP algorithm. First, the backward pass accepts infeasible state-control trajectories. Second, the rollout keeps the gaps open during the early "exploratory" iterations (as expected in multiple-shooting methods). We showcase the performance of our framework using different tasks. With our method, we can compute highly-dynamic maneuvers for legged robots (e.g. jumping, front-flip) in the order of milliseconds.

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Section: Introductionmentioning

confidence: 99%

Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control

Mastalli

Budhiraja

Merkt

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

213

226

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“…The rigid body algorithm library used for this simulation is Pinocchio [35]. This C++ library has been shown to be the fastest of its kind, with the Table 1 showcasing the average computation times of each necessary expression for the robot.…”

Section: ) Simulationmentioning

confidence: 99%

A Scalable Safety Critical Control Framework for Nonlinear Systems

et al. 2020

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There are two main approaches to safety-critical control. The first one relies on computation of control invariant sets and is presented in the first part of this work. The second approach draws from the topic of optimal control and relies on the ability to realize Model-Predictive-Controllers online to guarantee the safety of a system. In the second approach, safety is ensured at a planning stage by solving the control problem subject for some explicitly defined constraints on the state and control input. Both approaches have distinct advantages but also major drawbacks that hinder their practical effectiveness, namely scalability for the first one and computational complexity for the second. We therefore present an approach that draws from the advantages of both approaches to deliver efficient and scalable methods of ensuring safety for nonlinear dynamical systems. In particular, we show that identifying a backup control law that stabilizes the system is in fact sufficient to exploit some of the set-invariance conditions presented in the first part of this work. Indeed, one only needs to be able to numerically integrate the closed-loop dynamics of the system over a finite horizon under this backup law to compute all the information necessary for evaluating the regulation map and enforcing safety. The effect of relaxing the stabilization requirements of the backup law is also studied, and weaker but more practical safety guarantees are brought forward. We then explore the relationship between the optimality of the backup law and how conservative the resulting safety filter is. Finally, methods of selecting a safe input with varying levels of trade-off between conservatism and computational complexity are proposed and illustrated on multiple robotic systems, namely: a two-wheeled inverted pendulum (Segway), an industrial manipulator, a quadrotor, and a lower body exoskeleton.

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“…And later, we explain the visual task formulation within our multi-contact DDP. This work is based on the DDP solver implemented in Crocoddyl [19], which computes efficiently the rigid body dynamics and its derivatives using Pinocchio [20].…”

Section: Trajectory Validatormentioning

confidence: 99%

Motion Planning with Multi-Contact and Visual Servoing on Humanoid Robots

Giraud-Esclasse

Fernbach

Buondonno

et al. 2020

2020 IEEE/SICE International Symposium on System Integration (SII)

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This paper describes the implementation of a motion generation pipeline motivated by vision for a TALOS humanoid robot. From a starting configuration and given a set of visual features and their desired values, the problem is to find a motion which makes the robot reach the desired values of the visual features. In order to achieve a feasible Gazebo simulation with the targeted robot, we had to use a multicontact planner, a Differential Dynamic Programming (DDP) algorithm, and a stabilizer. The multicontact planner provides a set of contacts and dynamically consistent trajectories for the Center-Of-Mass (CoM) and the Center-Of-Pressure (CoP). It provides a structure to initialize a DDP algorithm which, in turn, provides a dynamically consistent trajectory for all the joints as it integrates all the dynamics of the robot, together with rigid contact models and the visual task. Tested on Gazebo the resulting trajectory had to be stabilized with a state-of-the-art algorithm to be successful.

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The Pinocchio C++ library : A fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives

Cited by 232 publications

References 28 publications

Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control

Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control

A Scalable Safety Critical Control Framework for Nonlinear Systems

Motion Planning with Multi-Contact and Visual Servoing on Humanoid Robots

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