RoboPapers Ep#14: In-Air Vehicle Maneuver for High-Speed Off-Road Navigation and VERTIFORMER
Two papers in one episode! Learn about how we can use small amounts of data to train transformers capable of doing truly impressive stuff.
Dom, cars don’t fly!—Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation
When pushing the speed limit for aggressive off-road navigation on uneven terrain, it is inevitable that vehicles may become airborne from time to time. During time-sensitive tasks, being able to fly over challenging terrain can also save time, instead of cautiously circumventing or slowly negotiating through. However, most off-road autonomy systems operate under the assumption that the vehicles are always on the ground and therefore limit operational speed. In this paper, we present a novel approach for in-air vehicle maneuver during high-speed off-road navigation. Based on a hybrid forward kinodynamic model using both physics principles and machine learning, our fixed-horizon, sampling-based motion planner ensures accurate vehicle landing poses and their derivatives within a short airborne time window using vehicle throttle and steering commands. We test our approach in extensive in-air experiments both indoors and outdoors, compare it against an error-driven control method, and demonstrate that precise and timely in-air vehicle maneuver is possible through existing ground vehicle controls.
VERTIFORMER: A Data-Efficient Multi-Task Transformer on Vertically Challenging Terrain
We propose VERTIFORMER, a novel data-efficient multi-task Transformer trained with only one hour of multi-modal data to address the challenges of applying Transformers for robot mobility on extremely rugged, vertically challenging, off-road terrain. With a Transformer encoder and decoder to predict the next robot pose, action, and terrain patch, VERTIFORMER employs a unified state space and missing modality infilling to respectively enhance dynamics understanding and enable a variety of off-road mobility tasks simultaneously, e.g., forward and inverse kinodynamics modeling. By leveraging this unified representation alongside modality infilling, it also achieves real-time task switching during inference for improved fault tolerance and better generalization to unseen environments. Furthermore, VERTIFORMER’s non-autoregressive design also mitigates computational bottlenecks and error propagation associated with autoregressive models. Our experiments offer insights into effectively utilizing Transformers for off-road robot mobility with limited data and demonstrate VERTIFORMER can facilitate multiple off-road mobility tasks onboard a physical mobile robot.
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