The Mini Wheelbot is a balancing, reaction wheel unicycle robot designed as a testbed for learning-based control. It is an unstable system with highly nonlinear yaw dynamics, non-holonomic driving, and discrete contact switches in a small, powerful, and rugged form factor. The Mini Wheelbot can use its wheels to stand up from any initial orientation - enabling automatic environment resets in repetitive experiments and even challenging half flips. We illustrate the effectiveness of the Mini Wheelbot as a testbed by implementing two popular learning-based control algorithms. First, we showcase Bayesian optimization for tuning the balancing controller. Second, we use imitation learning from an expert nonlinear MPC that uses gyroscopic effects to reorient the robot and can track higher-level velocity and orientation commands. The latter allows the robot to drive around based on user commands - for the first time in this class of robots. The Mini Wheelbot is not only compelling for testing learning-based control algorithms, but it is also just fun to work with, as demonstrated in the video of our experiments at https://youtu.be/_d7AqTRjz6g.

Approximating model predictive control (MPC) using imitation learning (IL) allows for fast control without solving expensive optimization problems online. However, methods that use neural networks in a simple L2-regression setup fail to approximate multi-modal (set-valued) solution distributions caused by local optima found by the numerical solver or nonconvex constraints, such as obstacles, significantly limiting the applicability of approximate MPC in practice. We solve this issue by using diffusion models to accurately represent the complete solution distribution (i.e., all modes) up to kilohertz sampling rates. This work shows that diffusion-based AMPC significantly outperforms L2-regression-based approximate MPC for multi-modal action distributions. In contrast to most earlier work on IL, we also focus on running the diffusion-based controller at a higher rate and in joint space instead of end-effector space. Additionally, we propose the use of gradient guidance during the denoising process to consistently pick the same mode in closed loop to prevent switching between solutions. We propose using the cost and constraint satisfaction of the original MPC problem during parallel sampling of solutions from the diffusion model to pick a better mode online. We evaluate our method on the fast and accurate control of a 7-DoF robot manipulator both in simulation and on hardware deployed at 250 Hz, achieving a speedup of more than 70 times compared to solving the MPC problem online and also outperforming the numerical optimization (used for training) in success ratio.

In this paper, we present a solution for simultaneous handling of large components with industrial robots performing synchronized motions with an AGV in flexible flow assembly. For this purpose, we implement an Extended Kalman Filter with a global localization system to track an AGV and multiple manipulators. We propose a model-predictive controller for force compliance and trajectory tracking in multi-robot cooperative, decentralized, and fast manipulation tasks. In order to show the effectiveness of our system, we assemble a truck windshield using two industrial robots and an AGV in motion. In our experiments, we reliably achieve assembly tolerances of 1.5mm at AGV velocities up to $400\frac{{{\text{mm}}}}{{\text{s}}}$. The presented system makes flexible assembly systems with AGVs and freely reconfigurable manipulators possible. It enables the automation of high variant, low volume, large size assembly tasks such as aircraft, truck or steel beam assembly, which are mostly manual processes at present.