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.

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.

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.

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.

We present a computational framework for robotic animation of real-world string puppets. Also known as marionettes, these articulated figures are typically brought to life by human puppeteers. The puppeteer manipulates rigid handles that are attached to the puppet from above via strings. The motions of the marionette are therefore governed largely by gravity, the pull forces exerted by the strings, and the internal forces arising from mechanical articulation constraints. This seemingly simple setup conceals a very challenging and nuanced control problem, as marionettes are, in fact, complex coupled pendulum systems. Despite this, in the hands of a master puppeteer, marionette animation can be nothing short of mesmerizing. Our goal is to enable autonomous robots to animate marionettes with a level of skill that approaches that of human puppeteers. To this end, we devise a predictive control model that accounts for the dynamics of the marionette and kinematics of the robot puppeteer. The input to our system consists of a string puppet design and a target motion, and our trajectory planning algorithm computes robot control actions that lead to the marionette moving as desired. We validate our methodology through a series of experiments conducted on an array of marionette designs and target motions. These experiments are performed both in simulation and using a physical robot, the human-sized, dual arm ABB YuMi ® IRB 14000.