Generative adversarial training of product of policies for robust and adaptive movement primitives

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which often are the kinematic and dynamic constraints of the system, are then only restored when synthesizing the motion, which introduces possibly heavy distortions. In this work, we propose to use those approximate trajectory distributions as close-to-optimal discriminators in the popular generative adversarial framework to stabilize and accelerate the learning procedure. The two problems of adaptability and robustness are addressed with our method. In order to adapt the motions to varying contexts, we propose to use a product of Gaussian policies defined in several parametrized task spaces. Robustness to perturbations and varying dynamics is ensured with the use of stochastic gradient descent and ensemble methods to learn the stochastic dynamics. Two experiments are performed on a 7-DoF manipulator to validate the approach.Comment: Source code can be found here : https://github.com/emmanuelpignat/tf_robot_learnin

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this paper, we introduce a novel active learning framework in order to improve the generalization capabilities of control policies. The proposed approach is based on the epistemic uncertainties of Bayesian Gaussian mixture models (BGMMs). We determine the new query point location by optimizing a closed-form information-density cost based on the quadratic R\'enyi entropy. Furthermore, to better represent uncertain regions and to avoid local optima problem, we propose to approximate the active learning cost with a Gaussian mixture model (GMM). We demonstrate our active learning framework in the context of a reaching task in a cluttered environment with an illustrative toy example and a real experiment with a Panda robot.Comment: Accepted for publication in IROS'2

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn the distribution of valid robot configurations under such constraints. It can generate configurations that are close to the constraint manifold. We present two applications of this method. First, by learning the conditional distribution with respect to the desired end-effector position, we can do fast inverse kinematics even for very high degrees of freedom (DoF) systems. Then, we use it to generate samples in sampling-based constrained motion planning algorithms to reduce the necessary projection steps, speeding up the computation. We validate the approach in simulation using the 7-DoF Panda manipulator and the 28-DoF humanoid robot Talos

Motion Mappings for Continuous Bilateral Teleoperation

Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between workspaces, such as object locations, it is particularly challenging to derive smooth motion mappings that fulfill different goals (e.g. picking objects with different poses on the two sides or passing through key points). Indeed, most state-of-the-art methods rely on mode switches, leading to a discontinuous, low-transparency experience. In this paper, we propose a unified formulation for position, orientation and velocity mappings based on the poses of objects of interest in the operator and robot workspaces. We apply it in the context of bilateral teleoperation. Two possible implementations to achieve the proposed mappings are studied: an iterative approach based on locally-weighted translations and rotations, and a neural network approach. Evaluations are conducted both in simulation and using two torque-controlled Franka Emika Panda robots. Our results show that, despite longer training times, the neural network approach provides faster mapping evaluations and lower interaction forces for the operator, which are crucial for continuous, real-time teleoperation.Comment: Accepted for publication at the IEEE Robotics and Automation Letters (RA-L

Product of experts for robot learning from demonstration

Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such that a non-expert user can teach the robot new tasks by showing examples. While the configuration of a manipulator is defined by its joint angles, postures and movements are often best explained under several task spaces. These task spaces are exploited by most of the existing LfD approaches. However, models are often learned independently in the different task spaces and only combined later, at the controller level. This simplification implies several limitations such as recovering the precision and hierarchy of the different tasks. They are also unable to uncover secondary task masked by the resolution of primary ones. In this thesis, we aim to overcome these limitations by proposing a consistent framework for LfD based on product of experts models (PoEs). In PoEs, data is modelled as a fusion from multiple sources or "experts". Each of them is giving an ``opinion'' on a different view or transformation of the data, which corresponds to different task spaces. Mathematically, the experts are probability density functions, which are multiplied together and renormalized. Distributions of two different nature are targeted in this thesis. In the first part of the thesis, PoEs are proposed to model distributions of robot configurations. These distributions are a key component of many LfD approaches. They are commonly used to define motions by introducing a dependence to time, as observation models in hidden-Markov models or transformed by a time-dependent basis matrix. Through multiple experiments, we show the advantages of learning models in several task spaces jointly in the PoE framework. We also compare PoE against more general techniques like variational autoencoders and generative adversarial nets. However, training a PoE requires costly approximations to which the performance can be very sensitive. An alternative approach to contrastive divergence is presented, by using variational inference and mixture model approximations. We also propose an extension to PoE with a nullspace structure (PoENS). This model can recover tasks that are masked by the resolution of higher-level objectives. In the second part of the thesis, PoEs are used to learn stochastic policies. We propose to learn motion primitives as distributions of trajectories. Instead of approximating complicated normalizing constants as in maximum entropy inverse optimal control, we propose to use a generative adversarial approach. The policy is parametrized as a product of Gaussian distributions of velocities, accelerations or forces, acting in different task spaces. Given an approximate and stochastic dynamic model of the system, the policy is trained by stochastic gradient descent, such that the distributions of rollouts match the distribution of demonstrations

Generative adversarial training of product of policies for robust and adaptive movement primitives

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinematic and dynamic constraints of the system, are then only restored when synthesizing the motion, which introduces possibly heavy distortions. In this work, we propose to use those approximate trajectory distributions as close-to-optimal discriminators in the popular generative adversarial framework to stabilize and accelerate the learning procedure. The two problems of adaptability and robustness are addressed with our method. In order to adapt the motions to varying contexts, we propose to use a product of Gaussian policies defined in several parametrized task spaces. Robustness to perturbations and varying dynamics is ensured with the use of stochastic gradient descent and ensemble methods to learn the stochastic dynamics. Two experiments are performed on a 7-DoF manipulator to validate the approach

Temporal Super-Resolution Microscopy Using a Hue-Encoded Shutter

Limited time-resolution in microscopy is an obstacle to many biological studies. Despite recent advances in hardware, digital cameras have limited operation modes that constrain frame-rate, integration time, and color sensing patterns. In this paper, we propose an approach to extend the temporal resolution of a conventional digital color camera by leveraging a multi-color illumination source. Our method allows imaging single-hue objects at increased frame-rate by trading spectral for temporal information (while retaining the ability to measure base hue). It also allows rapid switching to standard RGB acquisition. We evaluated the feasibility and performance of our method via experiments with mobile resolution targets. We observed a time-resolution increase by a factor 2.8 with a three-fold increase in temporal sampling rate. We further illustrate the use of our method to image the beating heart of a zebrafish larva, allowing the display of color or fast grayscale images. Our method is particularly well-suited to extend the capabilities of imaging systems where the flexibility of rapidly switching between high frame rate and color imaging are necessary

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn the distribution of valid robot configurations under such constraints. It can generate configurations that are close to the constraint manifold. We present two applications of this method. First, by learning the conditional distribution with respect to the desired end-effector position, we can do fast inverse kinematics even for very high degrees of freedom (DoF) systems. Then, we use it to generate samples in sampling-based constrained motion planning algorithms to reduce the necessary projection steps, speeding up the computation. We validate the approach in simulation using the 7-DoF Panda manipulator and the 28-DoF humanoid robot Talos