When looking at the costmaps generated from ACP in Fig. 1 with a running cost Eq. Similar to the max speed problem in Section VI-C, our proposed method has a problem being too myopic. Unlike Shi et al. T was set to correlate to approximately 6m long trajectories, as this covers almost all the drivable area in the camera view (see Fig. The approaches introduced in this paragraph are the extensions of the vanilla, Comparison to a simple road detection method, Any vision-based MDP problems, especially for camera-attached agents (e.g. On top of this AIRL, we perform MPC in image space (Section III) with a real-time-generated agent-view costmap. However, inverse reinforcement learning methods have This allows MPPI to compute trajectories that are better globally. [9] present a practical implementation of this method, dubbed Adversarial Inverse Reinforcement Learning (AIRL), alongside additional contributions that are not directly relevant to our work. Specifically in vision-based autonomous driving, if we train a deep NN by imitation learning and analyze an intermediate layer by reading the weights of the trained network and the activated neurons of it, we see the mapping converged to extracting important features that link the input and the output (Fig. The applications of these methods (e.g. Image space from a mounted camera on a robot is a local and fixed frame; i.e. provide planned control trajectories given an initial state and a cost function by solving the optimal control problem. However, the training of this architecture requires having a predetermined costmap to imitate and the track it was shown to generalize to had visually similar components (dirt track and black track borders) to recognize. For the TORCS dataset, we used the baseline test set collected by [4]. The major contributions over [5, 6] are using a Conv-LSTM layer to maintain the spatial information of states close together in time as well as a softmax attention mechanism applied to sparsify the Conv-LSTM layer. the training data did not include that specific image, or the trained network did not correctly learn the mapping from those input data to a corresponding output. The other Tracks have a similar issue to Track B, i.e. Recall that: 1. a) b) 2. In this way, we equally regard all the activated features as important ones. Compared to state-of-the-art methods, our proposed method is shown to generalize by generating usable costmaps in environments outside of its training data. 1). They find a weighted distribution of reward basis functions in an iterative way. Used in the Variational Discriminator Bottleneck (VDB) paper at ICLR.. Getting Set Up. Install rllab if not already. The data set consists of a vehicle running around a 170m-long track shown in Fig. γt is a discount. The following methods are evaluated in Section V. Pan et al. Then a cost weighted average is computed over the sampled controls. On the other hand, risk-neutral costmap allows the vehicle to drive at a high speed, but results in more risky behavior (e.g. Both approaches will result in a similar behavior of collision-averse navigation, but since our paper focuses on generating a costmap, just like typical IL settings, we assume the expert’s behavior is optimal. 2). The resulting costmap is used in conjunction with a Model Predictive Controller for real-time control and outperforms other state-of-the-art costmap generators combined with MPC in novel environments. Going off the image plane does not have a cost associated with it. arXiv 2019, Option-critic in cooperative multi-agent systems, A Fully Tensorized Recurrent Neural Network. Given. The key challenge was reward ambiguity: given an optimal policy for some MDP, there are many reward functions that could have led to this policy. In this work, we propose a method In this sense, the reward function in an MDP encodes the task of the agent. This feature extraction is further discussed in Section IV-C. Drews [7] provides a template NN architecture and training procedure to try to generalize costmap prediction to new environments in a method we call Attention-based Costmap Prediction (ACP). Our approach outperforms other state-of-the-art vision and deep-learning-based controllers in generalizing to new environments. We introduced an Approximate Inverse Reinforcement Learning framework using deep Convolutional Neural Networks. 4 and zero terminal cost for an autonomous driving task. J Chakravorty, N Ward, et al. Creating this ^πe then requires either solving or approximating a solution to a new MDP (X,U,T,^R,γ). 7, that our approach was the only method that was able to finish the whole lap of driving Track B and D.Compared to other methods, AIRL tended to hug track boundaries closely, presumably because of the sparsity of our costmaps. We perform a sampling-based stochastic optimal control in image space, which is perfectly suitable for our driver-view binary costmap. In this work, we leverage one such method, Adversarial Inverse Reinforcement Learning (AIRL), to propose an algorithm that learns hierarchical disentangled rewards with a policy over options. The problem of driving too close to the road boundaries or obstacles can be solved by introducing a risk-sensitive AIRL with a blur filter introduced in Section IV-C, but we can also solve the problem by converting our binary costmap to have smooth gradient information like in [7]. Apprenticeship learning about multiple intentions. ACP produced clear cost maps models in Track A (which it was trained on) and Track C, though Track C’s costmap was incorrect. As an analogy, our method is similar to learning the addition operator a+b=c whereas a prediction method would be similar to a mapping between numbers (a,b)→c. (d)d). This can be viewed as an implicit image segmentation done inside the deep convolutional neural network where the extracted features will depend on the task at hand. In general, a discrete-time optimal control problem whose objective is to minimize a task-specific cost function J(x,u) can be formulated as follows: subject to discrete time, continuous state-action dynamical system. Inverse reinforcement learning (IRL) algorithms can infer a reward from demonstrations in low-dimensional continuous control environments, but there has been little work on applying IRL to high-dimensional video games. Inverse Reinforcement Learning Michael Bloem and Nicholas Bambos Abstract—We extend the maximum causal entropy frame-work for inverse reinforcement learning to the inﬁnite time horizon discounted reward setting. The key idea is using a vision-based E2E Imitation Learning (IL) framework [22]. However, IL uses supervised learning to train a control policy and bypass this sample-inefficiency problem. Its extension to multi-agent settings, however, is difficult due to the more complex notions of rational behaviors. In another case, if the task is to perform autonomous lane-keeping, the boundaries of the lane will become important. Additionally, this decouples the state estimation and controller, allowing us to leverage standard state estimation techniques with a vision-based controller. [22] constructed a CNN that takes in RGB images and spits out control actions of throttle and steering angles for an autonomous vehicle. The concise description of this work is to create a NN that can take in camera images and output a costmap used by a MPC controller. MPC-based optimal controllers Inverse reinforcement learning (Ng & Russell, 2000) is the setting where an agent is trying to infer a reward function based on expert demonstrations. Our main contribution is learning an approximate, ‘generalizable’ costmap ‘from’ E2EIL with a minimal extra cost of adding a binary filter. MPPI uses a data-driven neural network model as a vehicle dynamics model. After this verification of MPPI parameters, we applied the same parameters to ACP. But the reward function approximator that enables transfer … Although our work relies on E2EIL and MPC, we tackle a totally different problem: IRL from E2EIL. The best job a learner can do is capped by the ability of a teacher since the objective of the IL setting is to mimic the expert’s behavior. We compare the methods mentioned in Section IV on the following scenarios: For a fair comparison, we trained all models with the same dataset used in [6]. California, USA, November 13-15, 2017, Proceedings, Vision-Based High-Speed Driving With a Deep Dynamic Observer, A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, International Journal of Robotics Research (IJRR), A. Giusti, J. Guzzi, D. Ciresan, F. He, J. P. Rodriguez, F. Fontana, M. Faessler, C. Forster, J. Schmidhuber, G. Di Caro, D. Scaramuzza, and L. Gambardella, A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots, B. Goldfain, P. Drews, C. You, M. Barulic, O. Velev, P. Tsiotras, and J. M. Rehg, AutoRally: an open platform for aggressive autonomous driving, Adam: A Method for Stochastic Optimization, Proceedings of the 3rd International Conference on Learning Representations (ICLR), Learning driving styles for autonomous vehicles from demonstration, 2015 IEEE International Conference on Robotics and Automation (ICRA), K. Lee, G. N. An, V. Zakharov, and E. A. Theodorou, Perceptual attention-based predictive control, Early failure detection of deep end-to-end control policy by reinforcement learning, 2019 International Conference on Robotics and Automation (ICRA), K. Lee, Z. Wang, B. I. Vlahov, H. K. Brar, and E. A. Theodorou, Ensemble bayesian decision making with redundant deep perceptual control policies, 18th IEEE International Conference on Machine Learning and Applications (ICMLA), S. Levine, C. Finn, T. Darrell, and P. Abbeel, ADAPS: Autonomous driving via principled simulations, Proceedings - IEEE International Conference on Robotics and Automation, A. Loquercio, E. Kaufmann, R. Ranftl, A. Dosovitskiy, V. Koltun, and D. Scaramuzza, Deep drone racing: from simulation to reality with domain randomization, Methods for interpreting and understanding deep neural networks, Algorithms for inverse reinforcement learning, Proceedings of the Seventeenth International Conference on Machine Learning, M. Ollis, W. H. Huang, M. Happold, and B. Ollis et al. More specifically, we focus on lane-keeping and collision checking like in [5, 6, 7, 22, 3]. In E2EIL, although the middle layer outputs meaningful features/heatmap, a small change of each middle layer’s activation coming from a novel input results in a random or false NN output. The perception pipeline was a Convolutional Neural Network (CNN), taking in raw images and producing a desired direction and velocity, trained in simulation on a large mixture of random backgrounds and gates. To repeat our problem statement, it is an inverse reinforcement learning problem of learning a cost function and the task is autonomous driving. It is important to note that near-perfect state estimation and a GPS track map is provided when MPPI is used as the expert, but as in [7], only body velocity, roll, and yaw from the state estimate is used when it is operating using vision. In a broad sense, the convolutional layer parts of the trained E2E network become a function that extracts important features in the input scene. The input might be new to the network, i.e. The data-driven neural network model takes in time, control and state (roll, body frame velocity in x,y and yaw rate) information as an input, and outputs the next state derivatives as described in [30]. For a manipulator reaching task or a drone flying task with obstacle avoidance, and after imitation learning of the tasks, our middle layer heatmap will output a binary costmap composed of specific features of obstacles (high cost) and other reachable/flyable regions (low cost). Second, the costmap generated in [7] has more gradient information than our binary costmap. However, without throwing it away, we can still use the CNN portion of the original network for feature extraction, which shows great generalizability after applying the binary filter we introduced in the AIRL costmap generation step. The training process is the same as the E2EIL controller; AIRL only requires a dataset of images, wheel speed sensor readings, and the expert’s optimal solution to train a costmap model (see Fig. Literally, E2EIL trains agents to directly output optimal control actions given image data from cameras; End(sensor reading) to End(control). Since the training data was collected at Track A (Fig. In other words, MPPI still plans in regular driving space, in the world coordinates. Our proposed approach requires one assumption: We then tuned MPPI with this model and drove it around Track B successfully for 10 laps straight before being manually stopped. As a result, with a risk-sensitive costmap, the optimal controller drives the vehicle in low-speed while gaining more safety (less collisions). 4: where Cs,Cc are coefficients that represent the penalty applied for speed and crash, respectively. Both approaches will result in a similar behavior of collision-averse navigation, but since our paper focuses on generating a costmap. A comparison of Reinforcement Learning and Inverse Reinforcement Learning in a diagram. Finally, we subtract [v′,u′] from [w2,h] and get the final [u,v]: We still use the same system dynamics in Eq. Also, to train a network which predicts the road, we would have to cover all possible kinds of roads to have good generalizability, but which increases the amount of labeling required dramatically. IRL is then learning a reward function ^R that describes the expert policy πe [2]. While it can achieve aggressive driving targets and was shown to handle various lighting conditions on the same track, it in general does not generalize to brand new tracks. Our approach provides solutions to these problems by leveraging the idea of using Deep Learning (DL) only in some blocks of autonomy, hence becomes more interpretable. Sequences of control vectors are sampled around a nominal trajectory and are propagated forward Δt in time using the dynamics model to generate state-action pairs that are input into the cost function. Inverse Reinforcement Learning6 (IRL) is a ﬁeld within machine learning that attempts to identify the implicit goals— more formally in the form of rewards—given demonstrations of expert behavior (e.g. Any vision-based MDP problems, especially for camera-attached agents (e.g. A. Stancil, Image-based path planning for outdoor mobile robots, 2008 IEEE International Conference on Robotics and Automation, Y. Pan, C. Cheng, K. Saigol, K. Lee, X. Yan, E. A. Theodorou, and B. Ross et al. The learning converged with a training loss of 4e−3 after 400 epochs. There is generally not a single reward function that can describe an expert behavior [2]. Using neural networks (NNs) for vision-based control has become ubiquitous in literature. arXiv Vanity renders academic papers from arXiv as responsive web pages so you don’t have to squint at a PDF. We then took all three methods and drove them on Tracks B, C, D, and E.For Tracks B, D, and E,we ran each algorithm in both clockwise and counter-clockwise for 20 lap attempts and measured the average travel distance. (2016a) present a theoretical discussion relating Generative Adversar- ial Networks (GANs) (Goodfellow et al.,2014), IRL, and energy-based models. For this reason, we cannot use the whole (same) architecture and its weights used in the E2EIL training phase. Iv-C Approximate Inverse Reinforcement Learning (Airl) Our method can be considered a mixture of the two previously mentioned; we will be using both E2E IL and an MPC controller. These results show an inability for ACP to generalize to varied different environments whereas our method produces similar looking costmaps throughout. In our work, as we deal with the state trajectory of the vehicle, we define the new origin at the bottom center of the image [w2,h], where h and w represents the height and width of the image, and rotate the axes by switching u′ and v′. Generative Adversarial Imitation Learning (GAIL) is an efficient way to learn sequential control strategies from demonstration. However, if the first pipeline fails to produce a correct objective function, the second part of path planning will calculate a wrong result and fail the task, no matter how well the controller or path planner is tuned. The exploration variance Σ represents the variance of the zero-mean Gaussian that MPPI uses when sampling random controls. In this work, we introduce a method for an inverse reinforcement learning problem and the task is vision-based autonomous driving. We would like to use all of the activated middle layer neurons to generate a costmap, but the magnitude of the activation is different for each feature. unclear costmaps. As MaxEnt IRL requires solving an integral over all possible trajectories for computing the partition function, it is only suitable for small scale … The proposed method allows to avoid manually designing a cost map that is generally required in supervised learning. However, these methods only control the steering angle and assume constant velocity. Aligning this … J in this optimal control settings corresponds to the negative reward (−R) in RL and F corresponds to the state transition function T in RL. Boots, Agile Autonomous Driving using End-to-End Deep Imitation Learning, A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Learning agents for uncertain environments, Proceedings of the eleventh annual conference on Computational learning theory, W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K. Müller, Explainable ai: interpreting, explaining and visualizing deep learning. Inverse reinforcement learning (IRL) (Russell, 1998; Ng & Russell, 2000) refers to the problem of inferring an expert’s reward function from demonstrations, which is a potential method for solv-ing the problem of reward engineering. For this navigation task, we followed the same definition of the system state and control in the MPPI paper [30] and [7]: x=[x,y,yaw,roll,vx,vy,˙yaw] is the vehicle state in a world coordinate frame and u is [throttle,steering]. Supplementary video: https://youtu.be/WyJfT5lc0aQ. The remaining of the paper is organized as follows: In Section II, we briefly review some preliminaries used in our work with some literature reviews. 5. The maximum entropy reinforcement learning (MaxEnt RL) objective is deﬁned as: max ˇ XT t=1 E (s t;a t)˘ˆ ˇ [r(s t;a)+ H(ˇ(js t))] (1) which augments the reward function with a causal entropy regularization term H(ˇ) = E ˇ[ logˇ(ajs)]. In RL, the reward function R is unknown to the learning agent; it receives observations at time t of the reward, rt, by moving through X and U. In this paper, we provide evidence of better performance than the expert teacher by showing a higher success rate of task completion when a task requires generalization to new environments. Unfortunately, we did not see the same track coverage with properly tuned MPPI. The binary filter outputs 1 if the activation is greater than 0. Here we extend these methods to the multiagent cooperative setting and show that they can better coordinate the behaviors of the agents. It works well in navigation along with a model predictive controller, but the MPC only solves an optimization problem with a local costmap. Pixel-wise heatmaps or activation maps have been widely used to interpret and explain the NN’s predictions and the information flow, given an input image [19, 25]. A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks. The track cost depends on the costmap and it is a binary grid map (0, 1) describes occupancy of features we want to avoid driving through, e.g. Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. This method allows for the less principled area of feature extraction and interpretation for autonomous driving to be done by the NN, and solve the stochastic optimal control problem in a principled way. Drews et al. Inverse einforrementc learning (IRL) deals with the problem of recovering the task representation ( i.e. Meta-learning is the problem where an agent is trained on some collection of different, but related environments or tasks, and is trying to learn a way to quickly adapt to new tasks. Despite these difficulties, IRL can be an extremely useful tool. In the case of autonomous driving, given a cost function to optimize and a vehicle dynamics model, we can compute an optimal solution via an optimal model predictive controller. 4 as Track A. For example, Subramanian. This will focus on the ability of a single network to generate reasonable costmaps even in a novel environment not seen during training. We first ran our costmap models AIRL and ACP on various datasets to show reasonable outputs in varied environments. Overall, ACP performed best on Track E, which is a simulated version of the track it was trained upon, Track A. Andrew Ng, Stuart Russel defines Inverse Reinforcement Learning (IRL) as. The coordinate transformation consists of 4 steps: In this work, we follow the convention in the computer graphics community and set the Z (optic)-axis as the vehicle’s longitudinal (roll) axis, the Y-axis as the axis normal to the road, the positive direction being upwards, and the X-axis as the axis perpendicular on the vehicle’s longitudinal axis, the positive direction pointing to the right side of vehicle. They were able to do this for many reasons. track boundaries or lane boundaries on the road. Moreover, we ran our algorithm in the late afternoon, which has very different lighting conditions compared to the training data as seen in Fig. Our approach uses the imitation learning framework, which does not require any extra labeling, and learns the task-related costmap which generalizes to various kinds of roads. The vehicle is located at the bottom middle of the costmap and black represents the low-cost region, white represents the high-cost. Since the end-to-end approach uses a totally blackbox model from sensor input to control output, it loses interpretability; when it fails, it is hard to tell if it comes from noise in the input, if the input is different from the training data, or if the model has just chosen a wrong control output due to ending training prematurely. If we split the typical autonomy pipeline in two, we can split it into a) a pipeline from sensor measurements to task-specific objective functions generation, and b) a pipeline from objective functions to corresponding optimal path and control. In classic path planning of robotic systems, sensor readings and optimization are all done in a world coordinate frame. Therefore, for our approach to generalize to a completely different dynamical environment, we simply need to change the dynamics used by MPPI and the approach continues to work. It can be considered similar to IL in that sense, as we could train agents to perform according to an expert behavior. In the next section, we show the experimental results of the vanilla AIRL and leave some room for the risk-sensitive version for future works. We can see in Fig. The MaxEnt RL framework relates the probability of sampling a trajec-tory by the optimal policy to the reward. The name of the game from this point: Inference of reward functions from demonstrations. Drews [7] uses an architecture that separates the vision-based control problem into a costmap generation task and then uses an MPC controller for generating the control. If we have an MPD and a policy , then for all , it is the case that and satisfy. The width between the boundaries varied from 0.5 m to 1.5 m and was in general much tighter than the off-road tracks. 3. From these reasons, E2E IL controllers are not widely used in the real-world applications, such as self-driving cars. Also, due to the fact that E2EIL can be taught from human data only [3], our approach can learn a cost function even without teaching specific task-related objectives to a model. [23] introduced an online Data Aggregation (DAgger) method, which mixes the expert’s policy and the learner’s policy to explore various situations like ϵ-greedy. We use Imitation Learning as a means to do Inverse Reinforcement Learning in order to create an approximate costmap generator for a visual navigation challenge. Implementation of Adversarial IRL (AIRL) with information bottleneck. Image source: Inverse Reinforcement Learning. Accordingly, IL provides a safer training process. Inverse reinforcement learning (IRL) was first described by Ng et al. Accordingly, E2EIL is vulnerable to out-of-training-data. The camera focal length is defined as f. . Inverse reinforce-ment learning provides a framework to automati-cally acquire suitable reward functions from ex-pert demonstrations. In this work, we propose adverserial inverse reinforcement learning (AIRL), a practical and scalable inverse reinforcement learning algorithm based on an adversarial reward learning formulation. In this work, we introduce a method for an inverse reinforcement learning problem and the task is vision-based autonomous driving. Boots, and E. A. Theodorou, Information theoretic MPC for model-based reinforcement learning, 2017 IEEE International Conference on Robotics and Automation (ICRA), B. Wymann, C. Dimitrakakisy, A. Sumnery, and C. Guionneauz, S. Xingjian, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, Convolutional lstm network: a machine learning approach for precipitation nowcasting, Advances in neural information processing systems, End-to-end learning of driving models from large-scale video datasets, Proceedings of the IEEE conference on computer vision and pattern recognition, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control, End-to-End Training of Deep Visuomotor Policies.

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