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Robotics (cs.RO)

Mon, 01 May 2023

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1.Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Control

Authors:Hojin Lee, Taekyung Kim, Jungwi Mun, Wonsuk Lee

Abstract: High-speed autonomous driving in off-road environments has immense potential for various applications, but it also presents challenges due to the complexity of vehicle-terrain interactions. In such environments, it is crucial for the vehicle to predict its motion and adjust its controls proactively in response to environmental changes, such as variations in terrain elevation. To this end, we propose a method for learning terrain-aware kinodynamic model which is conditioned on both proprioceptive and exteroceptive information. The proposed model generates reliable predictions of 6-degree-of-freedom motion and can even estimate contact interactions without requiring ground truth force data during training. This enables the design of a safe and robust model predictive controller through appropriate cost function design which penalizes sampled trajectories with unstable motion, unsafe interactions, and high levels of uncertainty derived from the model. We demonstrate the effectiveness of our approach through experiments on a simulated off-road track, showing that our proposed model-controller pair outperforms the baseline and ensures robust high-speed driving performance without control failure.

2.Learning Flight Control Systems from Human Demonstrations and Real-Time Uncertainty-Informed Interventions

Authors:Prashant Ganesh, J. Humberto Ramos, Vinicius G. Goecks, Jared Paquet, Matthew Longmire, Nicholas R. Waytowich, Kevin Brink

Abstract: This paper describes a methodology for learning flight control systems from human demonstrations and interventions while considering the estimated uncertainty in the learned models. The proposed approach uses human demonstrations to train an initial model via imitation learning and then iteratively, improve its performance by using real-time human interventions. The aim of the interventions is to correct undesired behaviors and adapt the model to changes in the task dynamics. The learned model uncertainty is estimated in real-time via Monte Carlo Dropout and the human supervisor is cued for intervention via an audiovisual signal when this uncertainty exceeds a predefined threshold. This proposed approach is validated in an autonomous quadrotor landing task on both fixed and moving platforms. It is shown that with this algorithm, a human can rapidly teach a flight task to an unmanned aerial vehicle via demonstrating expert trajectories and then adapt the learned model by intervening when the learned controller performs any undesired maneuver, the task changes, and/or the model uncertainty exceeds a threshold

3.A Comparison of Pneumatic Actuators for Soft Growing Vine Robots

Authors:Alexander M. Kübler, Cosima du Pasquier, Andrew Low, Betim Djambazi, Nicolas Aymon, Julian Förster, Nathaniel Agharese, Roland Siegwart, Allison M. Okamura

Abstract: Soft pneumatic actuators are used to steer soft growing "vine" robots while being flexible enough to undergo the tip eversion required for growth. They also meet the requirements to steer soft growing vine robots through challenging terrain. In this study, we compared the performance of three types of pneumatic actuators in terms of their ability to perform eversion, bending, dynamic motion, and force: the pouch motor, the cylindrical pneumatic artificial muscle (cPAM), and the fabric pneumatic artificial muscle (fPAM). The pouch motor is advantageous for prototyping due to its simple manufacturing process. The cPAM exhibits superior bending behavior and produces the highest forces, while the fPAM actuates fastest and everts at the lowest pressure. We evaluated a similar range of dimensions for each actuator type. Larger actuators can produce more significant deformations and forces, but smaller actuators inflate more quickly and require a lower eversion pressure. Since vine robots are lightweight, the effect of gravity on the functionality of different actuators is minimal. We developed a new analytical model that predicts the pressure-to-bending behavior of vine robot actuators. Using the actuator results, we designed and demonstrated a 4.8 m long vine robot equipped with highly maneuverable 60x60 mm cPAMs in a three-dimensional obstacle course. The vine robot was able to move around sharp turns, travel through a passage smaller than its diameter, and lift itself against gravity.