Dynamics Models in the Aggressive Off-Road Driving Regime

Current developments in autonomous off-road driving are steadily increasing performance through higher speeds and more challenging, unstructured environments. However, this operating regime subjects the vehicle to larger inertial effects, where consideration of higher-order states is necessary to avoid failures such as rollovers or excessive impact forces. Aggressive driving through Model Predictive Control (MPC) in these conditions requires dynamics models that accurately predict safety-critical information. This work aims to empirically quantify this aggressive operating regime and its effects on the performance of current models. We evaluate three dynamics models of varying complexity on two distinct off-road driving datasets: one simulated and the other real-world. By conditioning trajectory data on higher-order states, we show that model accuracy degrades with aggressiveness and simpler models degrade faster. These models are also validated across datasets, where accuracies over safety-critical states are reported and provide benchmarks for future work.

HOUND: An Open-Source, Low-cost Research Platform for High-speed Off-road Underactuated Nonholonomic Driving

Off-road vehicles are susceptible to rollovers in terrains with large elevation features, such as steep hills, ditches, and berms. One way to protect them against rollovers is ruggedization through the use of industrial-grade parts and physical modifications. However, this solution can be prohibitively expensive for academic research labs. Our key insight is that a software-based rollover-prevention system (RPS) enables the use of commercial-off-the-shelf hardware parts that are cheaper than their industrial counterparts, thus reducing overall cost. In this paper, we present HOUND, a small-scale, inexpensive, off-road autonomy platform that can handle challenging outdoor terrains at high speeds through the integration of an RPS. HOUND is integrated with a complete stack for perception and control, geared towards aggressive offroad driving. We deploy HOUND in the real world, at high speeds, on four different terrains covering 50 km of driving and highlight its utility in preventing rollovers and traversing difficult terrain. Additionally, through integration with BeamNG, a state-of-the-art driving simulator, we demonstrate a significant reduction in rollovers without compromising turning ability across a series of simulated experiments. Supplementary material can be found on our website, where we will also release all design documents for the platform: https://sites.google.com/view/prl-hound .

PuSHR: A Multirobot System for Nonprehensile Rearrangement

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 decentralized 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/DIWmZerF_O8.