Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system’s dynamics. This work addresses the problem of identifying multiple contexts of an AUV model. We build a simulation model of the robot from experimental data, and use it to fill in the missing data and generate different model contexts. We implement an architecture based on long-short-term-memory (LSTM) networks to learn the different contexts directly from the data. We show that the LSTM network can achieve high classification accuracy compared to baseline methods, showing robustness against noise and scaling efficiently on large datasets.
While GPs are the state of the art methods for robotic model learning, it is a well known fact that their computational complexity scales cubically with the number of training samples @math . This fact makes GPs unlikely to benefit from having big datasets that cover larger regions of the model's state space. A recent study @cite_2 shows that LSTM networks with a training time complexity of only @math , can outperform GPs for learning the inverse dynamics of a manipulator arm. The fact that LSTMs exploit the temporal correlations in the data makes this method suitable for learning dynamic models. Furthermore, their good computational efficiency and their scalability on big datasets is a major advantage for long-term learning settings.
@cite_2
2783813470 2762616984 1991667713 2172114485
Inverse dynamics model learning is crucial for modern robots where analytic models cannot capture the complex dynamics of compliant actuators, elasticities, mechanical inaccuracies, frictional effects or sensor noise. However, such models are highly nonlinear and millions of samples are needed to encode a large number of motor skills. Thus, current state of the art model learning approaches like Gaussian Processes which scale exponentially with the data cannot be applied. In this work, we developed an inverse dynamics model learning approach based on a long-short-term-memory (LSTM) network with a time complexity of O(n). We evaluated the approach on a KUKA robot arm that was used in object manipulation skills with various loads. In a comparison to Gaussian Processes we show that LSTM networks achieve better prediction performances and that they can be trained on large datasets with more than 100,000 samples in a few seconds. Moreover, due to the small training batch size of for example 128 samples, the network can be continuously improved in life-long learning scenarios. This paper presents a novel 3DOF pedestrian trajectory prediction approach for autonomous mobile service robots. While most previously reported methods are based on learning of 2D positions in monocular camera images, our approach uses range-finder sensors to learn and predict 3DOF pose trajectories (i.e. 2D position plus 1D rotation within the world coordinate system). Our approach, T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using long-term data from real-world robot deployments and aims to learn context-dependent (environment- and time-specific) human activities. Our approach incorporates long-term temporal information (i.e. date and time) with short-term pose observations as input. A sequence-to-sequence LSTM encoder-decoder is trained, which encodes observations into LSTM and then decodes as predictions. For deployment, it can perform on-the-fly prediction in real-time. Instead of using manually annotated data, we rely on a robust human detection, tracking and SLAM system, providing us with examples in a global coordinate system. We validate the approach using more than 15K pedestrian trajectories recorded in a care home environment over a period of three months. The experiment shows that the proposed T-Pose-LSTM model advances the state-of-the-art 2D-based method for human trajectory prediction in long-term mobile robot deployments. We consider an imitation learning approach to model robot point-to-point (also known as discrete or reaching) movements with a set of autonomous Dynamical Systems (DS). Each DS model codes a behavior (such as reaching for a cup and swinging a golf club) at the kinematic level. An estimate of these DS models are usually obtained from a set of demonstrations of the task. When modeling robot discrete motions with DS, ensuring stability of the learned DS is a key requirement to provide a useful policy. In this paper we propose an imitation learning approach that exploits the power of Control Lyapunov Function (CLF) control scheme to ensure global asymptotic stability of nonlinear DS. Given a set of demonstrations of a task, our approach proceeds in three steps: (1) Learning a valid Lyapunov function from the demonstrations by solving a constrained optimization problem, (2) Using one of the-state-of-the-art regression techniques to model an (unstable) estimate of the motion from the demonstrations, and (3) Using (1) to ensure stability of (2) during the task execution via solving a constrained convex optimization problem. The proposed approach allows learning a larger set of robot motions compared to existing methods that are based on quadratic Lyapunov functions. Additionally, by using the CLF formalism, the problem of ensuring stability of DS motions becomes independent from the choice of regression method. Hence it allows the user to adopt the most appropriate technique based on the requirements of the task at hand without compromising stability. We evaluate our approach both in simulation and on the 7 degrees of freedom Barrett WAM arm. Proposing a new parameterization to model complex Lyapunov functions.Estimating task-oriented Lyapunov functions from demonstrations.Ensuring stability of nonlinear autonomous dynamical systems.Applicability to any smooth regression method. Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by local learning, we propose a method to speed up standard Gaussian Process regression (GPR) with local GP models (LGP). The training data is partitioned in local regions, for each an individual GP model is trained. The prediction for a query point is performed by weighted estimation using nearby local models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online learning and prediction in real-time. Comparisons with other nonparametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and nu-SVR.