How effective are recent advancements in autonomous vehicle perception systems when applied to real-world autonomous vehicle control? While numerous vision-based autonomous vehicle systems have been trained and evaluated in simulated environments, there is a notable lack of real-world validation for these systems. This paper addresses this gap by presenting the real-world validation of state-of-the-art perception systems that utilize Behavior Cloning (BC) for lateral control, processing raw image data to predict steering commands. The dataset was collected using a scaled research vehicle and tested on various track setups. Experimental results demonstrate that these methods predict steering angles with low error margins in real-time, indicating promising potential for real-world applications.
K. J. Somaiya College Of Engineering（K. J. 索马亚工程学院）；Startup School India（印度创业学校）
Self Driving Car technology is a vehicle that guides itself without human conduction. The first truly autonomous cars appeared in the 1980s with projects funded by DARPA( Defense Advance Research Project Agency ). Since then a lot has changed with the improvements in the fields of Computer Vision and Machine Learning. We have used the concept of behavioral cloning to convert a normal RC model car into an autonomous car using Deep Learning technology
Learned path planners have attracted research interest due to their ability to model human driving behavior and rapid inference. Recent works on behavioral cloning show that simple imitation of expert observations is not sufficient to handle complex driving scenarios. Besides, predictions that land outside drivable areas can lead to potentially dangerous situations. This paper proposes a set of loss functions, namely Social loss and Road loss, which account for modelling risky social interactions in path planning. These losses act as a repulsive scalar field that surrounds non-drivable areas. …
Autonomous Systems Lab (ASL), ETH Zurich（苏黎世联邦理工学院自主系统实验室）；Toyota Research Institute (TRI)（丰田研究所）
Safe autonomous driving requires robust detection of other traffic participants. However, robust does not mean perfect, and safe systems typically minimize missed detections at the expense of a higher false positive rate. This results in conservative and yet potentially dangerous behavior such as avoiding imaginary obstacles. In the context of behavioral cloning, perceptual errors at training time can lead to learning difficulties or wrong policies, as expert demonstrations might be inconsistent with the perceived world state. In this work, we propose a behavioral cloning approach that can saf…
Indian Institute of Information Technology Allahabad（印度信息技术学院阿拉哈巴德分校）
With the growing phase of artificial intelligence and autonomous learning, the self-driving car is one of the promising area of research and emerging as a center of focus for automobile industries. Behavioral cloning is the process of replicating human behavior via visuomotor policies by means of machine learning algorithms. In recent years, several deep learning-based behavioral cloning approaches have been developed in the context of self-driving cars specifically based on the concept of transfer learning. Concerning the same, the present paper proposes a transfer learning approach using VGG…
The goal of our work is to use visual attention to enhance autonomous driving performance. We present two methods of predicting visual attention maps. The first method is a supervised learning approach in which we collect eye-gaze data for the task of driving and use this to train a model for predicting the attention map. The second method is a novel unsupervised approach where we train a model to learn to predict attention as it learns to drive a car. Finally, we present a comparative study of our results and show that the supervised approach for predicting attention when incorporated perform…
While behavior learning has made impressive progress in recent times, it lags behind computer vision and natural language processing due to its inability to leverage large, human-generated datasets. Human behaviors have wide variance, multiple modes, and human demonstrations typically do not come with reward labels. These properties limit the applicability of current methods in Offline RL and Behavioral Cloning to learn from large, pre-collected datasets. In this work, we present Behavior Transformer (BeT), a new technique to model unlabeled demonstration data with multiple modes. BeT retrofit…
Recent progress in end-to-end Imitation Learning approaches has shown promising results and generalization capabilities on mobile manipulation tasks. Such models are seeing increasing deployment in real-world settings, where scaling up requires robots to be able to operate with high autonomy, i.e. requiring as little human supervision as possible. In order to avoid the need for one-on-one human supervision, robots need to be able to detect and prevent policy failures ahead of time, and ask for help, allowing a remote operator to supervise multiple robots and help when needed. However, the blac…
We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models. We present extensive experiments on this finding, and we provide both intuitive insight and theoretical arguments distinguishing the properties of implicit models compared to their explicit counterparts, particularly with respect to approximating complex, potentially discontinuous and multi-valued (set-valued) functions. On robotic policy learning tasks we show that implicit behavioral cloning …
Indiana University Bloomington（印第安纳大学伯明顿分校）；Stony Brook University（石溪大学）
We present a visual imitation learning framework that enables learning of robot action policies solely based on expert samples without any robot trials. Robot exploration and on-policy trials in a real-world environment could often be expensive/dangerous. We present a new approach to address this problem by learning a future scene prediction model solely on a collection of expert trajectories consisting of unlabeled example videos and actions, and by enabling generalized action cloning using future image similarity. The robot learns to visually predict the consequences of taking an action, and…
In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing and training a neural network, following which, the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera. A novel coupled control law was formulated to generate longitudinal control commands on-…
University of Chicago（芝加哥大学）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
Learning from demonstration is widely used as an efficient way for robots to acquire new skills. However, it typically requires that demonstrations provide full access to the state and action sequences. In contrast, learning from observation offers a way to utilize unlabeled demonstrations (e.g., video) to perform imitation learning. One approach to this is behavioral cloning from observation (BCO). The original implementation of BCO proceeds by first learning an inverse dynamics model and then using that model to estimate action labels, thereby reducing the problem to behavioral cloning. Howe…
UC Berkeley（加州大学伯克利分校）；University of Pennsylvania（宾夕法尼亚大学）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海期智研究院）
Imitation learning trains policies to map from input observations to the actions that an expert would choose. In this setting, distribution shift frequently exacerbates the effect of misattributing expert actions to nuisance correlates among the observed variables. We observe that a common instance of this causal confusion occurs in partially observed settings when expert actions are strongly correlated over time: the imitator learns to cheat by predicting the expert's previous action, rather than the next action. To combat this "copycat problem", we propose an adversarial approach to learn a …
Cornell University（康奈尔大学）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
One of the main challenges in peg-in-a-hole (PiH) insertion tasks is in handling the uncertainty in the location of the target hole. In order to address it, high-dimensional sensor inputs from sensor modalities such as vision, force/torque sensing, and proprioception can be combined to learn control policies that are robust to this uncertainty in the target pose. Whereas deep learning has shown success in recognizing objects and making decisions with high-dimensional inputs, the learning procedure might damage the robot when applying directly trial- and-error algorithms on the real system. At …
Charles Sturt University（查尔斯特大学）；Worcester State University（伍斯特州立大学）；University of Kurdistan Hewler（库尔德斯坦赫勒大学）；University of New South Wales（新南威尔士大学）；Greater Western Sydney Area Health Services（大西悉尼地区卫生服务）；Central Coast Area Health（中央海岸地区卫生局）
Background and aim: Most of the Mixed Reality models used in the surgical telepresence are suffering from discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and the virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and remo…
Improving Behavioural Cloning with Positive Unlabeled Learning
University College Dublin（都柏林大学）；University College London（伦敦大学学院）；Dublin City University（都柏林城市大学）；Insight SFI Research Centre for Data Analytics（Insight SFI 数据分析研究中心）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）
Collective Behavior Clone with Visual Attention via Neural Interaction Graph Prediction
College of Computer Science and Technology at Zhejiang University（浙江大学计算机科学与技术学院）；Westlake University（西湖大学）；Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；University of Konstanz（康斯坦茨大学）
Stanford University（斯坦福大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Imitation learning has proven to be useful for many real-world problems, but approaches such as behavioral cloning suffer from data mismatch and compounding error issues. One attempt to address these limitations is the DAgger algorithm, which uses the state distribution induced by the novice to sample corrective actions from the expert. Such sampling schemes, however, require the expert to provide action labels without being fully in control of the system. This can decrease safety and, when using humans as experts, is likely to degrade the quality of the collected labels due to perceived actua…
Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所人机混合增强智能国家重点实验室）
Exploring multimodal implicit behavior learning for vehicle navigation in simulated cities
Systems Engineering Department, Federal University of Santa Catarina, Florianopolis, Brazil Faculty of Science；Engineering, Information Technology Åbo Akademi University, Finland
A variety of control tasks such as inverse kinematics (IK), trajectory optimization (TO), and model predictive control (MPC) are commonly formulated as energy minimization problems. Numerical solutions to such problems are well-established. However, these are often too slow to be used directly in real-time applications. The alternative is to learn solution manifolds for control problems in an offline stage. Although this distillation process can be trivially formulated as a behavioral cloning (BC) problem in an imitation learning setting, our experiments highlight a number of significant short…
