Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • Conference paper
    Pardo F, Levdik V, Kormushev P, 2020,

    Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

    , 34th AAAI Conference on Artificial Intelligence (AAAI 2020)
  • Book
    Deisenroth MP, Faisal AA, Ong CS, 2020,

    Mathematics for Machine Learning

    , Publisher: Cambridge University Press, ISBN: 9781108455145
  • Journal article
    Rakicevic N, Kormushev P, 2019,

    Active learning via informed search in movement parameter space for efficient robot task learning and transfer

    , Autonomous Robots, Vol: 43, Pages: 1917-1935, ISSN: 0929-5593

    Learning complex physical tasks via trial-and-error is still challenging for high-degree-of-freedom robots. Greatest challenges are devising a suitable objective function that defines the task, and the high sample complexity of learning the task. We propose a novel active learning framework, consisting of decoupled task model and exploration components, which does not require an objective function. The task model is specific to a task and maps the parameter space, defining a trial, to the trial outcome space. The exploration component enables efficient search in the trial-parameter space to generate the subsequent most informative trials, by simultaneously exploiting all the information gained from previous trials and reducing the task model’s overall uncertainty. We analyse the performance of our framework in a simulation environment and further validate it on a challenging bimanual-robot puck-passing task. Results show that the robot successfully acquires the necessary skills after only 100 trials without any prior information about the task or target positions. Decoupling the framework’s components also enables efficient skill transfer to new environments which is validated experimentally.

  • Conference paper
    Saputra RP, Rakicevic N, Kormushev P, 2019,

    Sim-to-real learning for casualty detection from ground projected point cloud data

    , 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Publisher: IEEE

    This paper addresses the problem of human bodydetection—particularly a human body lying on the ground(a.k.a. casualty)—using point cloud data. This ability to detect acasualty is one of the most important features of mobile rescuerobots, in order for them to be able to operate autonomously.We propose a deep-learning-based casualty detection methodusing a deep convolutional neural network (CNN). This networkis trained to be able to detect a casualty using a point-clouddata input. In the method we propose, the point cloud input ispre-processed to generate a depth image-like ground-projectedheightmap. This heightmap is generated based on the projecteddistance of each point onto the detected ground plane within thepoint cloud data. The generated heightmap—in image form—isthen used as an input for the CNN to detect a human bodylying on the ground. To train the neural network, we proposea novel sim-to-real approach, in which the network model istrained using synthetic data obtained in simulation and thentested on real sensor data. To make the model transferableto real data implementations, during the training we adoptspecific data augmentation strategies with the synthetic trainingdata. The experimental results show that data augmentationintroduced during the training process is essential for improvingthe performance of the trained model on real data. Morespecifically, the results demonstrate that the data augmentationson raw point-cloud data have contributed to a considerableimprovement of the trained model performance.

  • Conference paper
    AlAttar A, Rouillard L, Kormushev P, 2019,

    Autonomous air-hockey playing cobot using optimal control and vision-based Bayesian tracking

    , Towards Autonomous Robotic Systems, Publisher: Springer, ISSN: 0302-9743

    This paper presents a novel autonomous air-hockey playing collaborative robot (cobot) that provides human-like gameplay against human opponents. Vision-based Bayesian tracking of the puck and striker are used in an Analytic Hierarchy Process (AHP)-based probabilistic tactical layer for high-speed perception. The tactical layer provides commands for an active control layer that controls the Cartesian position and yaw angle of a custom end effector. The active layer uses optimal control of the cobot’s posture inside the task nullspace. The kinematic redundancy is resolved using a weighted Moore-Penrose pseudo-inversion technique. Experiments with human players show high-speed human-like gameplay with potential applications in the growing field of entertainment robotics.

  • Conference paper
    Falck F, Larppichet K, Kormushev P, 2019,

    DE VITO: A dual-arm, high degree-of-freedom, lightweight, inexpensive, passive upper-limb exoskeleton for robot teleoperation

    , TAROS: Annual Conference Towards Autonomous Robotic Systems, Publisher: Springer, ISSN: 0302-9743

    While robotics has made significant advances in perception, planning and control in recent decades, the vast majority of tasks easily completed by a human, especially acting in dynamic, unstructured environments, are far from being autonomously performed by a robot. Teleoperation, remotely controlling a slave robot by a human operator, can be a realistic, complementary transition solution that uses the motion intelligence of a human in complex tasks while exploiting the robot’s autonomous reliability and precision in less challenging situations.We introduce DE VITO, a seven degree-of-freedom, dual-arm upper-limb exoskeleton that passively measures the pose of a human arm. DE VITO is a lightweight, simplistic and energy-efficient design with a total material cost of at least an order of magnitude less than previous work. Given the estimated human pose, we implement both joint and Cartesian space kinematic control algorithms and present qualitative experimental results on various complex manipulation tasks teleoperating Robot DE NIRO, a research platform for mobile manipulation, that demonstrate the functionality of DE VITO. We provide the CAD models, open-source code and supplementary videos of DE VITO at http://www.imperial.ac.uk/robot-intelligence/robots/de_vito/.

  • Journal article
    Moriconi R, Kumar KSS, Deisenroth MP,

    High-dimensional Bayesian optimization with projections using quantile Gaussian processes

    , Optimization Letters, ISSN: 1862-4472
  • Journal article
    Creswell A, Bharath AA, 2019,

    Denoising adversarial autoencoders

    , IEEE Transactions on Neural Networks and Learning Systems, Vol: 30, Pages: 968-984, ISSN: 2162-2388

    Unsupervised learning is of growing interest becauseit unlocks the potential held in vast amounts of unlabelled data tolearn useful representations for inference. Autoencoders, a formof generative model, may be trained by learning to reconstructunlabelled input data from a latent representation space. Morerobust representations may be produced by an autoencoderif it learns to recover clean input samples from corruptedones. Representations may be further improved by introducingregularisation during training to shape the distribution of theencoded data in the latent space. We suggestdenoising adversarialautoencoders, which combine denoising and regularisation, shap-ing the distribution of latent space using adversarial training.We introduce a novel analysis that shows how denoising maybe incorporated into the training and sampling of adversarialautoencoders. Experiments are performed to assess the contri-butions that denoising makes to the learning of representationsfor classification and sample synthesis. Our results suggest thatautoencoders trained using a denoising criterion achieve higherclassification performance, and can synthesise samples that aremore consistent with the input data than those trained withouta corruption process.

  • Journal article
    Bertone G, Deisenroth MP, Kim JS, Liem S, de Austri RR, Welling Met al., 2019,

    Accelerating the BSM interpretation of LHC data with machine learning

    , PHYSICS OF THE DARK UNIVERSE, Vol: 24, ISSN: 2212-6864
  • Journal article
    Kormushev P, Ugurlu B, Caldwell DG, Tsagarakis NGet al., 2019,

    Learning to exploit passive compliance for energy-efficient gait generation on a compliant humanoid

    , Autonomous Robots, Vol: 43, Pages: 79-95, ISSN: 1573-7527

    Modern humanoid robots include not only active compliance but also passive compliance. Apart from improved safety and dependability, availability of passive elements, such as springs, opens up new possibilities for improving the energy efficiency. With this in mind, this paper addresses the challenging open problem of exploiting the passive compliance for the purpose of energy efficient humanoid walking. To this end, we develop a method comprising two parts: an optimization part that finds an optimal vertical center-of-mass trajectory, and a walking pattern generator part that uses this trajectory to produce a dynamically-balanced gait. For the optimization part, we propose a reinforcement learning approach that dynamically evolves the policy parametrization during the learning process. By gradually increasing the representational power of the policy parametrization, it manages to find better policies in a faster and computationally efficient way. For the walking generator part, we develop a variable-center-of-mass-height ZMP-based bipedal walking pattern generator. The method is tested in real-world experiments with the bipedal robot COMAN and achieves a significant 18% reduction in the electric energy consumption by learning to efficiently use the passive compliance of the robot.

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=954&limit=10&respub-action=search.html Current Millis: 1582878791099 Current Time: Fri Feb 28 08:33:11 GMT 2020