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Simon Zimmermann

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4 papers

1 author row

ICRA Conference 2023 Conference Paper

Gradient-Based Trajectory Optimization With Learned Dynamics

Bhavya Sukhija
Nathanael Köhler
Miguel Zamora
Simon Zimmermann
Sebastian Curi
Andreas Krause 0001
Stelian Coros

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 is to leverage machine learning techniques to learn a differentiable dynamics model of the system from data. In this work, we use trajectory optimization and model learning for performing highly dynamic and complex tasks with robotic systems in absence of accurate analytical models of the dynamics. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons, from data collected in only 25 minutes of interactions on two distinct robots: (i) the Boston Dynamics Spot and an (ii) RC car. Furthermore, we use the gradients of the neural network to perform gradient-based trajectory optimization. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car, and gives good performance in combination with trajectory optimization methods.

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IROS Conference 2022 Conference Paper

Differentiable Collision Avoidance Using Collision Primitives

Simon Zimmermann
Matthias Busenhart
Simon Huber
Roi Poranne
Stelian Coros

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 a bottleneck, as they do not scale well with the complexity of the robots or the environment. To improve performance, many different methods suggested to use collision primitives, i. e. simple shapes that approximate the more complex rigid bodies, and that are simpler to compute distances to and from. However, each pair of primitives requires its own specialized code, and certain pairs are known to suffer from numerical issues. In this paper, we propose an easy-to-use, unified treatment of a wide variety of primitives. We formulate distance computation as a minimization problem, which we solve iteratively. We show how to take derivatives of this minimization problem, allowing it to be seamlessly integrated into a trajectory optimization method. We demonstrate that the resulting method can be used to plan smooth and collision-free paths based on a variety of single- and multi-robot scenarios with different obstacles.

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IROS Conference 2021 Conference Paper

Animal Gaits on Quadrupedal Robots Using Motion Matching and Model-Based Control

Dongho Kang
Simon Zimmermann
Stelian Coros

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 demonstrate the efficacy of our control framework on a variety of quadrupedal robots in simulation. We show, in particular, that our approach can automatically reproduce key characteristics of animal motions, including speed-specific gaits, unscripted footfall patterns for nonperiodic motions, and natural small variations in overall body movements.

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ICRA Conference 2021 Conference Paper

Go Fetch! - Dynamic Grasps using Boston Dynamics Spot with External Robotic Arm

Simon Zimmermann
Roi Poranne
Stelian Coros

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. Consequently Spot’s behavior must largely be treated as a black box, which causes difficulties when combined with a moving payload such as a robotic arm. We overcome the arising challenges by building a model of the combined platform, fitting the corresponding model parameters using experimental data and a straight-forward optimization framework. We use this model to generate control commands for the physical platform using trajectory optimization. We demonstrate that even with a simple model, and control trajectories deployed in a feed-forward manner, the combined platform is capable of executing grasping tasks in a dynamic fashion. Furthermore, we show how the platform can use the additional degrees of freedom of the legs to extend the reachability of the arm.

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