Fast Damage Recovery in Robotics with the T-Resilience Algorithm

Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evaluate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches

Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped

Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but controllers learned in simulation often don't transfer to hardware. This necessitates optimization directly on hardware. However, collecting data on hardware can be expensive. This has led to a recent interest in adapting data-efficient learning techniques to robotics. One popular method is Bayesian Optimization (BO), a sample-efficient black-box optimization scheme, but its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge to reduce dimensionality in a meaningful way, with a focus on bipedal locomotion. In previous work, we proposed a transformation based on knowledge of human walking that projected a 16-dimensional controller to a 1-dimensional space. In simulation, this showed enhanced sample efficiency when optimizing human-inspired neuromuscular walking controllers on a humanoid model. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot -- in simulation and on hardware. We present three different walking controllers; two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.Comment: 8 pages, submitted to IEEE International Conference on Robotics and Automation 201

Deep Kernels for Optimizing Locomotion Controllers

Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive. Bayesian Optimization, a sample-efficient optimization framework, has recently been widely applied to address this problem, but further improvements in sample efficiency are needed for practical applicability to real-world robots and high-dimensional controllers. To address this, prior work has proposed using domain expertise for constructing custom distance metrics for locomotion. In this work we show how to learn such a distance metric automatically. We use a neural network to learn an informed distance metric from data obtained in high-fidelity simulations. We conduct experiments on two different controllers and robot architectures. First, we demonstrate improvement in sample efficiency when optimizing a 5-dimensional controller on the ATRIAS robot hardware. We then conduct simulation experiments to optimize a 16-dimensional controller for a 7-link robot model and obtain significant improvements even when optimizing in perturbed environments. This demonstrates that our approach is able to enhance sample efficiency for two different controllers, hence is a fitting candidate for further experiments on hardware in the future.Comment: (Rika Antonova and Akshara Rai contributed equally

Towards Naturalistic Interfaces of Virtual Reality Systems

Interaction plays a key role in achieving realistic experience in virtual reality (VR). Its realization depends on interpreting the intents of human motions to give inputs to VR systems. Thus, understanding human motion from the computational perspective is essential to the design of naturalistic interfaces for VR. This dissertation studied three types of human motions, including locomotion (walking), head motion and hand motion in the context of VR. For locomotion, the dissertation presented a machine learning approach for developing a mechanical repositioning technique based on a 1-D treadmill for interacting with a unique new large-scale projective display, called the Wide-Field Immersive Stereoscopic Environment (WISE). The usability of the proposed approach was assessed through a novel user study that asked participants to pursue a rolling ball at variable speed in a virtual scene. In addition, the dissertation studied the role of stereopsis in avoiding virtual obstacles while walking by asking participants to step over obstacles and gaps under both stereoscopic and non-stereoscopic viewing conditions in VR experiments. In terms of head motion, the dissertation presented a head gesture interface for interaction in VR that recognizes real-time head gestures on head-mounted displays (HMDs) using Cascaded Hidden Markov Models. Two experiments were conducted to evaluate the proposed approach. The first assessed its offline classification performance while the second estimated the latency of the algorithm to recognize head gestures. The dissertation also conducted a user study that investigated the effects of visual and control latency on teleoperation of a quadcopter using head motion tracked by a head-mounted display. As part of the study, a method for objectively estimating the end-to-end latency in HMDs was presented. For hand motion, the dissertation presented an approach that recognizes dynamic hand gestures to implement a hand gesture interface for VR based on a static head gesture recognition algorithm. The proposed algorithm was evaluated offline in terms of its classification performance. A user study was conducted to compare the performance and the usability of the head gesture interface, the hand gesture interface and a conventional gamepad interface for answering Yes/No questions in VR. Overall, the dissertation has two main contributions towards the improvement of naturalism of interaction in VR systems. Firstly, the interaction techniques presented in the dissertation can be directly integrated into existing VR systems offering more choices for interaction to end users of VR technology. Secondly, the results of the user studies of the presented VR interfaces in the dissertation also serve as guidelines to VR researchers and engineers for designing future VR systems

人間を模擬した知覚系を持つ2足ヒューマノイドロボットの自己位置推定機能に対する歩行動作の影響に関する研究

早大学位記番号:新8279早稲田大

Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search

One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fits best the current situation. We tackle this problem by introducing a novel acquisition function, called Most Likely Expected Improvement (MLEI), that combines the likelihood of the priors and the expected improvement. We evaluate this new acquisition function on a transfer learning task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has to learn to walk on flat ground and on stairs, with priors corresponding to different stairs and different kinds of damages. Our results show that MLEI effectively identifies and exploits the priors, even when there is no obvious match between the current situations and the priors.Comment: Accepted at ICRA 2018; 8 pages, 4 figures, 1 algorithm; Video at https://youtu.be/xo8mUIZTvNE ; Spotlight ICRA presentation https://youtu.be/iiVaV-U6Kq

Brain Tumour Classification Using Deep Features from Structural MRI

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