175,452 research outputs found
Cherry-Picking with Reinforcement Learning : Robust Dynamic Grasping in Unstable Conditions
Grasping small objects surrounded by unstable or non-rigid material plays a
crucial role in applications such as surgery, harvesting, construction,
disaster recovery, and assisted feeding. This task is especially difficult when
fine manipulation is required in the presence of sensor noise and perception
errors; errors inevitably trigger dynamic motion, which is challenging to model
precisely. Circumventing the difficulty to build accurate models for contacts
and dynamics, data-driven methods like reinforcement learning (RL) can optimize
task performance via trial and error, reducing the need for accurate models of
contacts and dynamics. Applying RL methods to real robots, however, has been
hindered by factors such as prohibitively high sample complexity or the high
training infrastructure cost for providing resets on hardware. This work
presents CherryBot, an RL system that uses chopsticks for fine manipulation
that surpasses human reactiveness for some dynamic grasping tasks. By
integrating imprecise simulators, suboptimal demonstrations and external state
estimation, we study how to make a real-world robot learning system sample
efficient and general while reducing the human effort required for supervision.
Our system shows continual improvement through 30 minutes of real-world
interaction: through reactive retry, it achieves an almost 100% success rate on
the demanding task of using chopsticks to grasp small objects swinging in the
air. We demonstrate the reactiveness, robustness and generalizability of
CherryBot to varying object shapes and dynamics (e.g., external disturbances
like wind and human perturbations). Videos are available at
https://goodcherrybot.github.io/
Principles of sensorimotor control and learning in complex motor tasks
The brain coordinates a continuous coupling between perception and action in the presence of uncertainty and incomplete knowledge about the world. This mapping is enabled by control policies and motor learning can be perceived as the update of such policies on the basis of improving performance given some task objectives. Despite substantial progress in computational sensorimotor control and empirical approaches to motor adaptation, to date it remains unclear how the brain learns motor control policies while updating its internal model of the world.
In light of this challenge, we propose here a computational framework, which employs error-based learning and exploits the brain’s inherent link between forward models and feedback control to compute dynamically updated policies. The framework merges optimal feedback control (OFC) policy learning with a steady system identification of task dynamics so as to explain behavior in complex object manipulation tasks. Its formalization encompasses our empirical findings that action is learned and generalised both with regard to a body-based and an object-based frame of reference. Importantly, our approach predicts successfully how the brain makes continuous decisions for the generation of complex trajectories in an experimental paradigm of unfamiliar task conditions. A complementary method proposes an expansion of the motor learning perspective at the level of policy optimisation to the level of policy exploration. It employs computational analysis to reverse engineer and subsequently assess the control process in a whole body manipulation paradigm.
Another contribution of this thesis is to associate motor psychophysics and computational motor control to their underlying neural foundation; a link which calls for further advancement in motor neuroscience and can inform our theoretical insight to sensorimotor processes in a context of physiological constraints. To this end, we design, build and test an fMRI-compatible haptic object manipulation system to relate closed-loop motor control studies to neurophysiology. The system is clinically adjusted and employed to host a naturalistic object manipulation paradigm on healthy human subjects and Friedreich’s ataxia patients. We present methodology that elicits neuroimaging correlates of sensorimotor control and learning and extracts longitudinal neurobehavioral markers of disease progression (i.e. neurodegeneration).
Our findings enhance the understanding of sensorimotor control and learning mechanisms that underlie complex motor tasks. They furthermore provide a unified methodological platform to bridge the divide between behavior, computation and neural implementation with promising clinical and technological implications (e.g. diagnostics, robotics, BMI).Open Acces
Mixtures of controlled Gaussian processes for dynamical modeling of deformable objects
Control and manipulation of objects is a highly relevant topic in Robotics research. Although significant advances have been made over the manipulation of rigid bodies, the manipulation of non-rigid objects is still challenging and an open problem. Due to the uncertainty of the outcome when applying physical actions to non-rigid objects, using prior knowledge on objects’ dynamics can greatly improve the control performance. However, fitting such models is a challenging task for materials such as clothing, where the state is represented by points in a mesh, resulting in very large dimensionality that makes models difficult to learn, process and predict based on measured data. In this paper, we expand previous work on Controlled Gaussian Process Dynamical Models (CGPDM), a method that uses a non-linear projection of the state space onto a much smaller dimensional latent space, and learns the object dynamics in the latent space. We take advantage of the variability in training data by employing Mixture of Experts (MoE), and we devise theory and experimental validations that demonstrate significant improvements in training and prediction times, plus robustness and error stability when predicting deformable objects exposed to disparate movement ranges.This work was partially developed in the context of the project CLOTHILDE (”CLOTH manIpulation Learning from DEmonstrations”), which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Advanced Grant agreement No 741930). We would like to thank the members of the HCRL Lab and the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin for their feedback during the development of this work.Peer ReviewedPostprint (published version
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
We propose a technique for multi-task learning from demonstration that trains
the controller of a low-cost robotic arm to accomplish several complex picking
and placing tasks, as well as non-prehensile manipulation. The controller is a
recurrent neural network using raw images as input and generating robot arm
trajectories, with the parameters shared across the tasks. The controller also
combines VAE-GAN-based reconstruction with autoregressive multimodal action
prediction. Our results demonstrate that it is possible to learn complex
manipulation tasks, such as picking up a towel, wiping an object, and
depositing the towel to its previous position, entirely from raw images with
direct behavior cloning. We show that weight sharing and reconstruction-based
regularization substantially improve generalization and robustness, and
training on multiple tasks simultaneously increases the success rate on all
tasks
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