Robots in the real world experience wear and tear, leading to changing system dynamics. This challenge is particularly exacerbated for non-rigid systems such as soft robots or robotic systems made of metamaterials with hysteresis. This setting results in a challenging problem for most learning-based controllers that typically rely on the assumption that the system dynamics remain fixed over time. In the absence of explicit mechanisms to account for this change in dynamics, learning-based control algorithms show considerable degradation in performance over time. In this work, we consider a particular class of dynamics shift in under-actuated systems, that is localized to the dynamics of the fully actuated robot itself, while independently leaving the dynamics of the environment unchanged. This captures real-world phenomena such as fatigue or hysteresis in robotic systems. In this setting, we propose an efficient algorithm that can account for dynamics shift. Using a simple calibration procedure, we propose a technique for learning a non-linear “action-translation” model that can capture the localized shift in dynamics. This enables continual learning and transfer despite considerable dynamics shift during the learning process. We demonstrate the efficacy of this procedure on several tasks in simulation, as well as a real-world robotic system - a 4 DoF electrically driven handed shearing auxetic (HSA) platform.

We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardware. Centralized replanning approaches become intractable even for small-sized problems whereas decentralized approaches often get stuck in deadlocks. Our key insight is that by carefully assigning pushing tasks to robots, we could reduce the complexity of the rearrangement task, enabling robust performance via scalable decentralized control. Based on this insight, we built PuSHR, a system that optimally assigns pushing tasks and trajectories to robots offline, and performs trajectory tracking via decentralized control online. Through an ablation study in simulation, we demonstrate that PuSHR dominates baselines ranging from purely centralized to fully decentralized in terms of success rate and time efficiency across challenging tasks with up to 4 robots. Hardware experiments demonstrate the transfer of our system to the real world and highlight its robustness to model inaccuracies. Our code can be found at https://github.com/prl-mushr/pushr, and videos from our experiments at https://youtu.be/nyUn9mHoR8Y.