6 accepted papers
Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate analytical models of the dynamics, yet some aspects of the physical world can only be captured to a limited extent. An alternative approach i…
A central aspect of robotic motion planning is collision avoidance, where a multitude of different approaches are currently in use. Optimization-based motion planning is one method, that often heavily relies on distance computations between robots and obstacles. These computations can easily become…
In this paper, we explore the challenge of generating animal-like walking motions for legged robots. To this end, we propose a versatile and robust control pipeline that combines a state-of-the-art model-based controller with a data-driven technique that is commonly used in computer animation. We de…
Due to their complex dynamics and high-dimensional configuration spaces, non-rigid objects such as cables, garments, bedding and various food items remain notoriously challenging for robots to manipulate effectively. In this letter, we therefore develop, validate and analyze model-based optimal cont
We combine Boston Dynamics Spot® with a light-weight, external robot arm to perform dynamic grasping maneuvers. While Spot is a reliable, robust and easy-to-control mobile robot, these highly desirable qualities come with the price that the control access granted to the user is restricted. Consequen…
We present an optimization framework for grasp and motion planning in the context of robotic assembly. Typically, grasping locations are provided by higher level planners or as input parameters. In contrast, our mathematical model simultaneously optimizes motion trajectories, grasping locations, and