Jan Carius

Last Name

Carius

First Name

Jan

ORCID

0000-0001-7158-9404

Show all metadata

Search Results

Publications 1 - 10 of 13

Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation
Sleiman, Jean-Pierre; Carius, Jan; Grandia, Ruben; et al. (2019)
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
We present a reformulation of a contact-implicit optimization (CIO) approach that computes optimal trajectories for rigid-body systems in contact-rich settings. A hard-contact model is assumed, and the unilateral constraints are imposed in the form of complementarity conditions. Newton's impact law is adopted for enhanced physical correctness. The optimal control problem is formulated as a multi-staged program through a multiple-shooting scheme. This problem structure is exploited within the FORCES Pro framework to retrieve optimal motion plans, contact sequences and control inputs with increased computational efficiency. We investigate our method on a variety of dynamic object manipulation tasks, performed by a six degrees of freedom robot. The dynamic feasibility of the optimal trajectories, as well as the repeatability and accuracy of the task-satisfaction are verified through simulations and real hardware experiments on one of the manipulation problems.
Constrained stochastic optimal control with learned importance sampling: A path integral approach
Carius, Jan; Ranftl, René; Farshidian, Farbod; et al. (2022)
The International Journal of Robotics Research
Modern robotic systems are expected to operate robustly in partially unknown environments. This article proposes an algorithm capable of controlling a wide range of high-dimensional robotic systems in such challenging scenarios. Our method is based on the path integral formulation of stochastic optimal control, which we extend with constraint-handling capabilities. Under our control law, the optimal input is inferred from a set of stochastic rollouts of the system dynamics. These rollouts are simulated by a physics engine, placing minimal restrictions on the types of systems and environments that can be modeled. Although sampling-based algorithms are typically not suitable for online control, we demonstrate in this work how importance sampling and constraints can be used to effectively curb the sampling complexity and enable real-time control applications. Furthermore, the path integral framework provides a natural way of incorporating existing control architectures as ancillary controllers for shaping the sampling distribution. Our results reveal that even in cases where the ancillary controller would fail, our stochastic control algorithm provides an additional safety and robustness layer. Moreover, in the absence of an existing ancillary controller, our method can be used to train a parametrized importance sampling policy using data from the stochastic rollouts. The algorithm may thereby bootstrap itself by learning an importance sampling policy offline and then refining it to unseen environments during online control. We validate our results on three robotic systems, including hardware experiments on a quadrupedal robot.
Autonomous Mission with a Mobile Manipulator - A Solution to the MBZIRC
Carius, Jan; Wermelinger, Martin; Rajasekaran, Balasubramanian; et al. (2017)
Springer Proceedings in Advanced Robotics ~ Field and Service Robotics
This work presents the system and approach we employed to tackle thesecond challenge of the Mohamed Bin Zayed International Robotics Challenge(MBZIRC). The goal of this challenge is to find a tool panel on a field, pickan appropriate wrench from the panel, and operate a valve stem therewith. Forthis purpose we use a task-oriented field robot, based on Clearpath Husky with acustomized series elastic arm, that can be deployed for versatile purposes. However,to be competitive in a robotic challenge, further specialization and improvementsare necessary to achieve a certain task faster and more reliably. A high emphasis isput on designing a system that can operate fully autonomously and independentlyrespond if a subtask was not executed successfully. Moreover, the operator can easilymonitor the system through a graphical user interface and, if desired, interact with therobot. We present our algorithms to explore the field, detect the panel, and navigateto it. Furthermore, we use a support vector machine based object detection methodto locate the valve stem and wrenches on the panel for visual servoing. Finally, weshow the advantages of a force controllable manipulator to handle the valve stemwith a tool. This system demonstrated its applicability when fulfilling the entire taskfully autonomously during both trials of the Grand Challenge of the MBZIRC 2017.
Trajectory optimization for legged robots with slipping motions
Carius, Jan; Ranftl, René; Koltun, Vladlen; et al. (2019)
IEEE Robotics and Automation Letters
The dynamics of legged systems are characterized by under-actuation, instability, and contact state switching. We present a trajectory optimization method for generating physically consistent motions under these conditions. By integrating a custom solver for hard contact forces in the system dynamics model, the optimal control algorithm has the authority to freely transition between open, closed, and sliding contact states along the trajectory. Our method can discover stepping motions without a predefined contact schedule. Moreover, the optimizer makes use of slipping contacts if a no-slip condition is too restrictive for the task at hand. Additionally, we show that new behaviors like skating over slippery surfaces emerge automatically, which would not be possible with classical methods that assume stationary contact points. Experiments in simulation and on hardware confirm the physical consistency of the generated trajectories. Our solver achieves iteration rates of 40 Hz for a 1 s horizon and is therefore fast enough to run in a receding horizon setting.
CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge
Tranzatto, Marco; Mascarich, Frank; Bernreiter, Lukas; et al. (2022)
Field Robotics
Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems, as underground settings present key challenges that can render robot autonomy hard to achieve. This problem has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, we present the CERBERUS system-of-systems, as a unified strategy for subterranean exploration using legged and flying robots. Our proposed approach relies on ANYmal quadraped as primary robots, exploiting their endurance and ability to traverse challenging terrain. For aerial robots, we use both conventional and collision-tolerant multirotors to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, we developed a complementary multimodal sensor-fusion approach, utilizing camera, LiDAR, and inertial data for resilient robot pose estimation. Individual robot pose estimates are refined by a centralized multi-robot map-optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined coordinate frame. Furthermore, a unified exploration path-planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication among team agents and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station and wireless “breadcrumb” nodes deployed by the legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge’s Tunnel and Urban Circuit events, along with the current limitations and the lessons learned for the benefit of the community.
Imitation Learning from MPC for Quadrupedal Multi-Gait Control
Reske, Alexander; Carius, Jan; Ma, Yuntao; et al. (2021)
2021 IEEE International Conference on Robotics and Automation (ICRA)
We present a learning algorithm for training a single policy that imitates multiple gaits of a walking robot. To achieve this, we use and extend MPC-Net, which is an Imitation Learning approach guided by Model Predictive Control (MPC). The strategy of MPC-Net differs from many other approaches since its objective is to minimize the control Hamiltonian, which derives from the principle of optimality. To represent the policies, we employ a mixture-of-experts network (MEN) and observe that the performance of a policy improves if each expert of a MEN specializes in controlling exactly one mode of a hybrid system, such as a walking robot. We introduce new loss functions for single- and multi-gait policies to achieve this kind of expert selection behavior. Moreover, we benchmark our algorithm against Behavioral Cloning and the original MPC implementation on various rough terrain scenarios. We validate our approach on hardware and show that a single learned policy can replace its teacher to control multiple gaits.
Trajectory Optimization with Implicit Hard Contacts
Carius, Jan; Ranftl, René; Koltun, Vladlen; et al. (2018)
IEEE Robotics and Automation Letters
We present a contact invariant trajectory optimization formulation to synthesize motions for legged robotic systems. The method is capable of finding optimal trajectories subject to whole body dynamics with hard contacts. Contact switches are determined automatically. We make use of concepts from bilevel optimization to find gradients of the system dynamics including the constraint forces and subsequently solve the optimal control problem with the unconstrained iLQR algorithm. Our formulation achieves fast computation times and scales well with the number of contact points. The physical correctness of the produced trajectories is verified through experiments in simulation and on real hardware. We showcase our method on a single legged hopper for which jumping and forward hopping motions are synthesized with an arbitrary number of contact switches. The jumping trajectories can be tracked on the robot and allow it to safely liftoff and land.
Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds
Neunert, Michael; Stäuble, Markus; Giftthaler, Markus; et al. (2018)
IEEE Robotics and Automation Letters
Locomotion Planning through a Hybrid Bayesian Trajectory Optimization
Seyde, Tim; Carius, Jan; Grandia, Ruben; et al. (2019)
2019 International Conference on Robotics and Automation (ICRA)
Locomotion planning for legged systems requires reasoning about suitable contact schedules. The contact se- quence and timings constitute a hybrid dynamical system and prescribe a subset of achievable motions. State-of-the- art approaches cast motion planning as an optimal control problem. In order to decrease computational complexity, one common strategy separates footstep planning from motion optimization and plans contacts using heuristics. In this paper, we propose to learn contact schedule selection from high- level task descriptors using Bayesian Optimization. A bi-level optimization is defined in which a Gaussian Process model predicts the performance of trajectories generated by a motion planning nonlinear program. The agent, therefore, retains the ability to reason about suitable contact schedules, while explicit computation of the corresponding gradients is avoided. We delineate the algorithm in its general form and provide results for planning single-legged hopping. Our method is capable of learning contact schedule transitions that align with human intuition. It performs competitively against a heuristic baseline in predicting task appropriate contact schedules.
Deployment of an autonomous mobile manipulator at MBZIRC
Carius, Jan; Wermelinger, Martin; Rajasekaran, Balasubramanian; et al. (2018)
Journal of Field Robotics

Publications 1 - 10 of 13

Jan Carius

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results