12 accepted papers
Loader cranes with multiple actuated joints are complex systems to be operated by humans. Development of advanced assistance functions, such as end-effector velocity control in Cartesian space allows for utilizing the machine to its full speed and potential, wherein actuator limits, load balance, an
This paper proposes a novel control method for an autonomous wheel loader, enabling time-efficient navigation to an arbitrary goal pose. Unlike prior works which combine high-level trajectory planners with Model Predictive Control (MPC), we directly enhance the planning capabilities of MPC by incorp
Loading multiple different materials with wheel loaders is a challenging task because various materials require different loading techniques. It’s, therefore, difficult to find a single controller capable of handling them all. One solution is to use a base controller and fine-tune it for different m…
Safe motion control in unknown environments is one of the challenging tasks in robotics, such as autonomous navigation. Control Barrier Function (CBF), as a strong math-ematical tool, has been widely used in many safety-critical systems to satisfy safety requirements. However, there are only a handf…
The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robotics research. However, the computational complexity of GPs has made policy search a highly time and memory consuming pr…
This paper presents a robust machine learning framework for modeling and control of hydraulic actuators. We identify several important challenges concerning learning accurate models of the dynamics for real machines, including noise and uncertainty in state measurements, nonlinear effects, input del
We have recently proposed two pile loading controllers that learn from human demonstrations: a neural network (NNet) [1] and a random forest (RF) controller [2]. In the field experiments the RF controller obtained clearly better success rates. In this work, the previous findings are drastically revi…
This work introduces a learning-based pile loading controller for autonomous robotic wheel loaders. Controller parameters are learnt from a small number of demonstrations for which low level sensor (boom angle, bucket angle and hydrostatic driving pressure), egocentric video frames and control signa…
This paper presents the development and testing of end-to-end Neural Network (NN) controllers for automated pile loading with a robotic wheel loader. NNs were trained using the Learning from Demonstration approach, i.e. by first recording sensor and control signals during manually-driven pile loadin…
Motion planning for manipulators under task space constraints is difficult as it constrains the joint configurations to always lie on an implicitly defined manifold. It is possible to view task constrained motion planning as an optimization problem with non-linear equality constraints, which can be…
This paper addresses the problem of pallet picking by an Articulated-Frame-Steering (AFS) hydraulic machine. We propose a macro-micro visual mobile manipulation architecture, where a smooth switching logic navigates the robot to pick an object. The state space is divided into several regions dependi
This paper, as a generalization of our previous works, presents a unified time-optimal path-following controller for Wheeled Mobile Robots (WMRs). Unlike other path-following controllers, we solve the path-following problem for all common categories of WMRs such as car-like, differential, omnidirect…