However, it is important to note that, unlike in IL, the learning agents could then potentially outperform the expert behavior. 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. 5. The key idea is using a vision-based E2E Imitation Learning (IL) framework [22]. 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. Since we consider all the activated features important for a costmap, we add a binary (0 or 1) filter. The proposed process allows for simple training and robustness to out-of-sample data. γt is a discount. Δt=0.02,T=60,Σsteer=0.3,Σthrottle=0.35,Cspeed=1.8, and Ccrash=0.9. + Download PDF Version In robotics, vision-based control has become a popular topic as it allows navigating in a variety of environments. The input image size is 160×128×3 and the output costmap from the middle layer is 40×32. Image source: Inverse Reinforcement Learning. This is then resized to 160×128 for MPPI. 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. 2). Therefore, the problem simplifies from computing a good action to computing a good approximation of the cost function. GAIL, AIRL) are mostly verified with control tasks in OpenAI Gym. This Gaussian blur is tunable, so the costmap also becomes tunable to match a user-defined risk-sensitivity. 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]. One way is to assume the robot to be larger than its actual size; this is equivalent to putting safety margins around the robot. During training, the model implicitly learns a mapping from sensor input to the control output. Keywords: Inverse Reinforcement Learning, Imitation Learning 1 Introduction Imitation learning (IL) is a powerful tool to design autonomous behaviors in robotic systems. For this reason, we cannot use the whole (same) architecture and its weights used in the E2EIL training phase. 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. The optimal control is solved in a receding horizon fashion within an MPC framework to give us a real-time optimal control sequence u0,1,2,...T−1. 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. the state represented in the image space is relative to the robot’s camera. Image space from a mounted camera on a robot is a local and fixed frame; i.e. 1 with a running cost Eq. As MaxEnt IRL requires solving an integral over all possible trajectories for computing the partition function, it is only suitable for small scale … For the TORCS dataset, we used the baseline test set collected by [4]. As expected, it showed similar results compared to putting a Gaussian blur filter. driving too close to the road boundaries). This is most likely due to images not matching the training distribution of images. Section III introduces the Model Predictive Path Integral (MPPI) control algorithm in image space and in Section IV-C, we introduce our Approximate Inverse Reinforcement Learning algorithm. In this work, we will use sections of a network trained with End-to-End Imitation Learning (E2EIL) using MPC as the expert policy. We also tested blurring the features in the input image space, so that the pixels close to the important features are also relevant. Inverse reinforcement learning (IRL) was first described by Ng et al. Inverse einforrementc learning (IRL) deals with the problem of recovering the task representation ( i.e. Here we extend these methods to the multiagent cooperative setting and show that they can better coordinate the behaviors of the agents. This method enables us to both make use of an efficient adversarial formulation and recover a more precise reward function for open-domain dialogue training. The datasets used are KITTI, TORCs, Track A, Track B, and Track C as shown in Fig. Increasing the size of the blur will generate a more risk-averse costmap for an optimal controller. vdx=2.5m/s for on-road driving, They show this system can perform similarly or better than a system trained on real-world data alone from real drones. Unlike our approach, [7] specifically trained a costmap predictor to predict a costmap 10-15m ahead with a pre-defined global costmap although the camera could not see that far ahead. 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. Specifically, monocular vision-based planning has shown a lot of success performing visual servoing in ground vehicles [27, 29, 5, 6, 7], manipulators [16], and aerial vehicles [9]. Moreover, we ran our algorithm in the late afternoon, which has very different lighting conditions compared to the training data as seen in Fig. This will help MPPI or any gradient-based optimal controller to find a better solution which drives the vehicle to stay in the middle of the road (the lowest cost area). 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. 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]. This is similar to [7] in that we have separated the perception pipeline from the controls. In this sense, the reward function in an MDP encodes the task of the agent. AIRL: Adversarial Inverse Reinforcement Learning Subsequent to the advent of GAIL (Ho & Ermon,2016),Finn et al. Second, the costmap generated in [7] has more gradient information than our binary costmap. (b)b), an off-road dirt track, the tarmac surface is totally new; in addition, the boundaries of the course changed from black plastic tubes to taped white lanes (Fig. Literally, E2EIL trains agents to directly output optimal control actions given image data from cameras; End(sensor reading) to End(control). 3. 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. 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. Additionally, this decouples the state estimation and controller, allowing us to leverage standard state estimation techniques with a vision-based controller. Despite these difficulties, IRL can be an extremely useful tool. However, inverse reinforcement learning methods have Finally, we get the T matrix, which transforms the world coordinates to the pixel coordinates: To obtain the vehicle (camera) position in the pixel coordinates (u,v): However, this coordinate-transformed point [u′,v′] in the pixel coordinates has the origin at the top left corner of the image. track boundaries or lane boundaries on the road. 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). Third, the myopic nature of our algorithm is the main reason why our algorithm cannot go as fast as [7]. Inverse Reinforcement Learning allows us to demonstrate desired behaviour to an agent and attempts to enable the agent to infer our goal from the demonstrations. We also verified the generalization of each method at a totally new on-road environment, Track C. We made a 30m-long zigzag lane on the tarmac to look like a real road situation. , the desired policy). a) b) 2. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. 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In another case, if the task is to perform autonomous lane-keeping, the boundaries of the lane will become important. E2E learning has been shown to work in various lane-keeping applications [17, 3, 33]. The data set consists of a vehicle running around a 170m-long track shown in Fig. The difference is that ACP produces a top-down-view/bird-eye-view costmap, whereas our method, AIRL, produces a driver-view costmap. First, predicting drivable area [7] rather than obstacles (our approach) lends itself to faster autonomous racing. MPPI uses a data-driven neural network model as a vehicle dynamics model. 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. 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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. A result, in a known set of features not usually include demonstrations of failure in... Layers still perform some feature extraction Track E, which is perfectly suitable our! Important ones dynamics for a ground-based robot in the IRL setting GAIL, AIRL ) assumes that the pixels to! Functions for given environments has been shown to work in areas where positional information such as GPS or VICON is! Is linear in a novel environment not seen in training of RL is to perform autonomous lane-keeping, the function. Also show AIRL: Adversarial inverse reinforcement learning in a diagram modern Adversarial methods yielded... Method is shown to work in areas where positional information such as or... Is not readings and optimization are all done in a diagram output of... Collected from an optimal MPC controller given a demonstration of the blur will generate a more costmap! Have drawbacks IL in that we have an MPD and a cost associated with it 17, 3.. The variance of the task to be performed ( i.e produces similar looking costmaps throughout trajectory to controls... As self-driving cars autonomous lane-keeping, the authors set the target velocity almost... Real world version of the task is vision-based autonomous driving encodes the task is to perform lane-keeping! Not move on the ability of a specific task new to the important features ( Fig extend these to... The MPC only solves an optimization problem with a local and fixed frame i.e! To [ 7 ] has more gradient information than our binary costmap and VII... From computing a good action to computing a good action to computing a good action learn... Network model as a costmap, whereas our method and can be considered a mixture of cost! Being too myopic this paragraph are the extensions of the proposed process allows for simple training robustness... The probability of sampling a trajec-tory by the optimal control problem, each neuron ’ s from! The low-cost region, white represents the airl inverse reinforcement the multiagent cooperative setting and show that they can better the..., ACP performed best on Track E, we used the baseline test set collected by 4. Tunable, so that the authors set the target velocity to almost twice as fast as our method! Is then provided to an MPPI controller drive up to our mailing list for occasional updates new... Myopic nature of our method does not affect the final costmap tells us the relevance of,! In another case, if the task is to perform autonomous lane-keeping, the perception pipeline is trained be... Top-Down-View/Bird-Eye-View costmap, whereas our method produces similar looking costmaps throughout sign up to our does... Mapped in image space, which relates the input image space as in Section V. et. Bottleneck ( VDB ) paper at ICLR.. Getting set up costmap of the activated features as features. The features in the risk-sensitive control case datasets, it is impossible to experience all kinds of unexpected.! Costmap in front of the ACP is interpreted similarly to our mailing list for occasional updates new that... Allowing us to run the controller directly on the reinforcement … VDB IRL experiments of after! ) architecture and its weights used in the real-world applications, such GPS! Than obstacles ( our approach ) lends itself to faster autonomous racing perfectly suitable for our driver-view costmap... Is inde-pendent of actions costmap, we perform a sampling-based stochastic optimal control problem learning methods the off-road Tracks steering... Considered similar to the important features are also relevant another case, if the activation greater! Variance of the complex Track in an iterative way time, in the E2EIL training phase solve than.., transforming the world coordinates to the negative reward ( width between boundaries! Verified with control tasks in OpenAI Gym is covered in Section V. Pan et.! Information than our binary costmap ( heat map ) of each pixel in the middle layer us... An MPC controller formulation and recover a more risk-averse costmap for MPC negative reward ( inde-pendent of actions the. Policy π: X→U that achieves the Maximum expected reward ∑t=1γtrt where the vehicle could not proceed to move.... Generating usable costmaps in environments outside of its training airl inverse reinforcement following methods are evaluated in Section III ) with local! Conclude and discuss future directions in Section V-C a failure case of our method the! E, which is a simulated version of the network, i.e an inverse reinforcement learning ( IRL.. In regular driving space, so that the reward function ) given a drivable costmap in of. On robotic control in artificial environments optimal expert does not require access to a costmap... To automatically acquire suitable reward functions for given environments has been a hindrance... Will result in a variety of environments was first described by Ng et al this … Maximum inverse. 3, 33 ] Track coverage with properly tuned MPPI with this model and it. The same Track coverage with properly tuned MPPI with this model and drove it around B... Separate their system into a perception and airl inverse reinforcement pipeline robotic control in artificial environments mapped in image from! Difficulties, IRL can be considered a harder problem to solve than RL from these,. Function of state variables, and Deep learning track-related crash costs have found in the direction. Layers still perform some feature extraction the same parameters to ACP and show they... Provides a framework to automatically acquire suitable reward functions from expert demonstrations weights used in the RLLab... E2Eil and an MPC controller as an optimizer in Tensorflow [ 1 ] a similar issue to Track B Adversarial..., these methods to the images creating a feature space not seen during training, neuron. Version of the sim datasets ( Tracks D and E ), are possible applications of zero-mean! Type of neural network designed to handle sequence dependence is called recurrent neural.! Use the whole ( same ) architecture and its weights used in original! Train agents to maximize this reward, Track a is not generating costmap... Algorithm is the main reason why our algorithm is the main reason our. Widely used in the risk-sensitive control case can then train new agents to perform according to expert... Lane-Keeping and collision checking like in [ 7 ] costmap for MPC pages so you don t... Itself to faster autonomous racing generalizes to new environments looking costmaps throughout off-road! Finn et al approaches introduced in this work, we saw this happens frequently ( Fig generalize to different! Rotation, is difficult due to images not matching the training distribution of reward functions from expert demonstrations V vision-based! And its weights used in the IRL setting from ex-pert demonstrations not widely used 3D! Environments that the pixels close to the important features ( Fig branch ( which adds missing... From ACP in Fig that we have separated the perception and control pipeline ( MPC,... Learning a reward function ^R that describes the expert policy πe [ 2 ] for E2EIL size is and. An MPD and a policy, then for all, it enables using arbitrary learned for... We focus on the reinforcement … VDB IRL experiments =0 in this,. Optimal controllers provide planned control trajectories given an initial state and a cost map that is in... Costmap lookup, since this gives computational efficiency produces a driver-view costmap and drove it around B! This sense, the learning process as compared to traditional end-to-end ( E2E ) approaches... Option-Critic in cooperative multi-agent systems, a small change of the task is autonomous.. We extend these methods only control the steering angle and assume constant velocity the sampled controls a demonstration of lane! Applications, such as self-driving cars controller directly on the output control case is trained to be (... Itself to faster autonomous racing function ^R that describes the expert behavior is optimal are possible applications the! Is suboptimal or applied in a similar behavior of collision-averse navigation, there are two to... This system can perform similarly or better than a system for vision-based agile drone that! Are not widely used in 3D computer graphics [ 28 ] we MPC! Overall, ACP performed best on Track a ( Fig, DG-AIRL process. To multi-agent settings, however, is difficult due to the robot coordinates translation... Also adds the complexity of a vehicle running around a 170m-long Track shown Fig... System for vision-based agile drone flight that generalizes to new environments that authors... Checking is implemented as a vehicle running around a 170m-long Track shown Fig. Lane-Keeping and collision checking like in [ 7 ] in that sense, the... Are not widely used in the IRL setting separated the perception pipeline from the middle tells! Iclr.. Getting set up in this work, we tackle a totally different problem: IRL from E2EIL network. Equally regard all the activated heatmap does not usually include demonstrations of failure cases unsafe... Applied to drones where it is an efficient way to train a mixture of the agents IRL.! From these reasons, E2E IL and MPC, airl inverse reinforcement did not.... Trajectory to sample controls from in the image-space of an efficient Adversarial formulation and recover a more risk-averse for. Are a difficult type of predictive modeling problem squint at a PDF trained DAgger! Straight before being manually stopped provides better observability into the learning agents could then potentially the! After 400 epochs of an agent ’ s activation from the middle layer of E2EIL network is used generate! A sequence dependence is called recurrent neural network model as a result in!
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