In shared autonomy, the user input is blended with the assistive motion to accomplish a task where the user goal is typically unknown to the robot. Transparency between the human and robot is essential for effective collaboration. Prior works have provided methods for the robot to infer the user goal; however, they are usually dependent on the distance between the robot and object, which may not be directly associated with the real-time user control intention and thus cause low control feelings. Here, we propose a real-time goal prediction method driven by assistive motion generated by learning from demonstration (LfD) allowing more reactive assistive behaviors. This LfD-generated assistive motion is blended with the user input based on goal predictions to achieve targeted tasks. The LfD policy was learned offline and used with different users. To evaluate our proposed method, we compared it with a state-of-the-art Partially Observable Markov Decision Process (POMDP) based method using a distance cost, and a direct control method (i.e., joystick). A pilot study (N = 6) was conducted to control a 6-DoF Kinova Mico robotic arm to carry out three tasks: (1) reaching-and-grasping, (2) pouring, and (3) object-returning with the three control methods. We used both objective and subjective measures in the comparative study. Results show that our method has the shortest task completion time, the lowest amount of joystick control inputs among all three control methods, as well as a significantly lower angular difference between the user input and assistive motion compared to the POMDP-based method. Besides, it obtains the highest subjective score in the user preference and perceived speed ratings, and the second-highest in the control feeling and the robot did what I wanted ratings.

We propose an exploration method that incorporates lookahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies. Our skills are multi-goal policies learned in isolation in simpler environments using existing multigoal RL formulations, analogous to options or macroactions. Coarse skill dynamics, i.e., the state transition caused by a (complete) skill execution, are learnt and are unrolled forward during lookahead search. Policy search benefits from temporal abstraction during exploration, though itself operates over low-level primitive actions, and thus the resulting policies does not suffer from suboptimality and inflexibility caused by coarse skill chaining. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parametrized skills as building blocks of the policy itself, as opposed to guiding exploration. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parameterized skills as building blocks of the policy itself, as opposed to guiding exploration.

Performing safe and efficient lane changes is a crucial feature for creating fully autonomous vehicles. Recent advances have demonstrated successful lane following behavior using deep reinforcement learning, yet the interactions with other vehicles on-road for lane changes are rarely considered. In this paper, we design a hierarchical Deep Reinforcement Learning (DRL) algorithm to learn lane change behaviors in dense traffic. By breaking down overall behavior to sub-policies, faster and safer lane change actions can be learned. We also apply temporal and spatial attention to the DRL architecture, which helps the vehicle focus more on surrounding vehicles and leads to smoother lane change behavior. We conduct our experiments in the TORCS simulator and the results outperform the state-of-the-art deep reinforcement learning algorithm in various lane change scenarios.

A safe and robust on-road navigation system is a crucial component of achieving fully automated vehicles. NVIDIA recently proposed an End-to-End algorithm that can directly learn steering commands from raw pixels of a front camera by using one convolutional neural network. In this paper, we leverage auxiliary information aside from raw images and design a novel network structure, called Auxiliary Task Network (ATN), to help boost the driving performance while maintaining the advantage of minimal training data and an End-to-End training method. In this network, we introduce human prior knowledge into vehicle navigation by transferring features from image recognition tasks. Image semantic segmentation is applied as an auxiliary task for navigation. We consider temporal information by introducing an LSTM module and optical flow to the network. Finally, we combine vehicle kinematics with a sensor fusion step. We discuss the benefits of our method over state-of-the-art visual navigation methods both in the Udacity simulation environment and on the real-world Comma.ai dataset.

Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd.

We address the problem of learning autonomous driving behaviors in urban intersections using deep reinforcement learning (DRL). DRL has become a popular choice for creating autonomous agents due to its success in various tasks. However, as the problems tackled become more complex, the number of training iterations necessary increase drastically. Curriculum learning has been shown to reduce the required training time and improve the performance of the agent, but creating an optimal curriculum often requires human handcrafting. In this work, we learn a policy for urban intersection crossing using DRL and introduce a method to automatically generate the curriculum for the training process from a candidate set of tasks. We compare the performance of the automatically generated curriculum (AGC) training to those of randomly generated sequences and show that AGC can significantly reduce the training time while achieving similar or better performance.

Predicting lane-changing intentions has long been a very active area of research in the autonomous driving community. However, most of the literature has focused on individual vehicles and did not consider both the neighbor information and the accumulated effects of vehicle history trajectories when making the predictions. We propose to apply a surrounding-aware LSTM algorithm for predicting the intention of a vehicle to perform a lane change that takes advantage of both vehicle past trajectories and their neighbor's current states. We trained the model on real-world lane changing data and were able to show in simulation that these two components can lead not only to higher accuracy, but also to earlier lane-changing prediction time, which plays an important role in potentially improving the autonomous vehicle's overall performance.

For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with one another to avoid collisions, while heading toward their respective destinations. Previous approaches have used handcrafted functions based on proximity to model human-human and human-robot interactions. However, these approaches can only model simple interactions and fail to generalize for complex crowded settings. In this paper, we develop an approach that models the joint distribution over future trajectories of all interacting agents in the crowd, through a local interaction model that we train using real human trajectory data. The interaction model infers the velocity of each agent based on the spatial orientation of other agents in his vicinity. During prediction, our approach infers the goal of the agent from its past trajectory and uses the learned model to predict its future trajectory. We demonstrate the performance of our method against a state-of-the-art approach on a public dataset and show that our model outperforms when predicting future trajectories for longer horizons.

Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat dynamic obstacles as static, frequent re-planning is unavoidable as the obstacles move, and their solutions are generally sub-optimal and can be incomplete. To achieve both optimality and completeness, it is necessary to consider the time dimension during planning. The notion of adaptive dimensionality has been successfully used in high-dimensional motion planning such as manipulation of robot arms, but has not been used in the context of path planning in dynamic environments. In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model. Specifically, our approach considers the time dimension only in those regions of the environment where a potential collision may occur, and plans in a low-dimensional state-space elsewhere. We show that our approach is complete and is guaranteed to find a solution, if one exists, within a cost sub-optimality bound. We experimentally validate our method on the problem of 3D vehicle navigation (x, y, heading) in dynamic environments. Our results show that the presented approach achieves substantial speedups in planning time over 4D heuristic-based A*, especially when the resulting plan deviates significantly from the one suggested by the heuristic.

We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the commonly used version of Dynamic Movement Primitives (DMPs) implement this minimization in the way they adapt demonstrations, for a particular choice of the Hilbert space norm. The generalization to arbitrary norms enables the robot to select a more appropriate norm for the task, as well as learn how to adapt the demonstration from the user. Our experiments show that this can significantly improve the robot's ability to accurately generalize the demonstration.

In this video, we present approaches for learning throwing and catching skills. We first show how a hitting skill (i.e., table tennis) can be learned using a combination of imitation and reinforcement learning. This hitting skill is subsequently generalized to a catching skill. Secondly, we show how a robot can adapt a throwing skill to new targets. Finally, we demonstrate that a BioRob and a Barrett WAM can play catch together using the previously acquired skills.

Opponent modeling is a critical mechanism in repeated games. It allows a player to adapt its strategy in order to better respond to the presumed preferences of his opponents. We introduce a new modeling technique that adaptively balances exploitability and risk reduction. An opponent’s strategy is modeled with a set of possible strategies that contain the actual strategy with a high probability. The algorithm is safe as the expected payoff is above the minimax payoff with a high probability, and can exploit the opponents’ preferences when sufficient observations have been obtained. We apply them to normal-form games and stochastic games with a finite number of stages. The performance of the proposed approach is first demonstrated on repeated rock-paper-scissors games. Subsequently, the approach is evaluated in a human-robot table-tennis setting where the robot player learns to prepare to return a served ball. By modeling the human players, the robot chooses a forehand, backhand or middle preparation pose before they serve. The learned strategies can exploit the opponent’s preferences, leading to a higher rate of successful returns.

Opponent modeling is a critical mechanism in repeated games. It allows a player to adapt its strategy in order to better respond to the presumed preferences of its opponents. We introduce a modeling technique that adaptively balances safety and exploitability. The opponent's strategy is modeled with a set of possible strategies that contains the actual one with high probability. The algorithm is safe as the expected payoff is above the minimax payoff with high probability, and can exploit the opponent's preferences when sufficient observations are obtained. We apply the algorithm to a robot table-tennis setting where the robot player learns to prepare to return a served ball. By modeling the human players, the robot chooses a forehand, backhand or middle preparation pose before they serve. The learned strategies can exploit the opponent's preferences, leading to a higher rate of successful returns.

Playing table tennis is a difficult task for robots, especially due to their limitations of acceleration. A key bottleneck is the amount of time needed to reach the desired hitting position and velocity of the racket for returning the incoming ball. Here, it often does not suffice to simply extrapolate the ball's trajectory after the opponent returns it but more information is needed. Humans are able to predict the ball's trajectory based on the opponent's moves and, thus, have a considerable advantage. Hence, we propose to incorporate an anticipation system into robot table tennis players, which enables the robot to react earlier while the opponent is performing the striking movement. Based on visual observation of the opponent's racket movement, the robot can predict the aim of the opponent and adjust its movement generation accordingly. The policies for deciding how and when to react are obtained by reinforcement learning. We conduct experiments with an existing robot player to show that the learned reaction policy can significantly improve the performance of the overall system.

Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients, many of these problems may be addressed by constraining the information loss. In this paper, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It can be shown to work well on typical reinforcement learning benchmark problems.

Hitting and batting tasks, such as tennis forehands, ping-pong strokes, or baseball batting, depend on predictions where the ball can be intercepted and how it can properly be returned to the opponent. These predictions get more accurate over time, hence the behaviors need to be continuously modified. As a result, movement templates with a learned global shape need to be adapted during the execution so that the racket reaches a target position and velocity that will return the ball over to the other side of the net or court. It requires altering learned movements to hit a varying target with the necessary velocity at a specific instant in time. Such a task cannot be incorporated straightforwardly in most movement representations suitable for learning. For example, the standard formulation of the dynamical system based motor primitives (introduced by Ijspeert et al. [1]) does not satisfy this property despite their flexibility which has allowed learning tasks ranging from locomotion to kendama. In order to fulfill this requirement, we reformulate the Ijspeert framework to incorporate the possibility of specifying a desired hitting point and a desired hitting velocity while maintaining all advantages of the original formulation. We show that the proposed movement template formulation works well in two scenarios, i.e., for hitting a ball on a string with a table tennis racket at a specified velocity and for returning balls launched by a ball gun successfully over the net using forehand movements. All experiments were carried out on a Barrett WAM using a four camera vision system.

Although human beings see and move slower than table tennis or baseball robots, they manage to outperform such robot systems. One important aspect of this better performance is the human movement generation. In this paper, we study trajectory generation for table tennis from a biomimetic point of view. Our focus lies on generating efficient stroke movements capable of mastering variations in the environmental conditions, such as changing ball speed, spin and position. We study table tennis from a human motor control point of view. To make headway towards this goal, we construct a trajectory generator for a single stroke using the discrete movement stages hypothesis and the virtual hitting point hypothesis to create a model that produces a human-like stroke movement. We verify the functionality of the trajectory generator for a single forehand stroke both in a simulation and using a real Barrett WAM™.