1,946 research outputs found

    Data-Driven Grasp Synthesis - A Survey

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    We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

    Fall Prediction and Controlled Fall for Humanoid Robots

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    Humanoids which resemble humans in their body structure and degrees of freedom are anticipated to work like them within infrastructures and environments constructed for humans. In such scenarios, even humans who have exceptional manipulation, balancing, and locomotion skills are vulnerable to fall, humanoids being their approximate imitators are no exception to this. Furthermore, their high center of gravity position in relation to their small support polygon makes them more prone to fall, unlike other robots such as quadrupeds. The consequences of these falls are so devastating that it can instantly annihilate both the robot and its surroundings. This has become one of the major stumbling blocks which humanoids have to overcome to operate in real environments. As a result, in this thesis, we have strived to address the imminent fall over of humanoids by developing different control techniques. The fall over problem as such can be divided into three subissues: fall prediction, controlled fall, and its recovery. In the presented work, the first two issues have been addressed, and they are presented in three parts. First, we define what is fall over for humanoids, different sources for it to happen, the effect fall over has both on the robot and to its surroundings, and how to deal with them. Following which, we give a brief introduction to the overall system which includes both the hardware and software components which have been used throughout the work for varied purposes. Second, the first sub-issue is addressed by proposing a generic method to predict the falling over of humanoid robots in a reliable, robust, and agile manner across various terrains, and also amidst arbitrary disturbances. The aforementioned characteristics are strived to attain by proposing a prediction principle inspired by the human balance sensory systems. Accordingly, the fusion of multiple sensors such as inertial measurement unit and gyroscope (IMU), foot pressure sensor (FPS), joint encoders, and stereo vision sensor, which are equivalent to the human\u2019s vestibular, proprioception, and vision systems are considered. We first define a set of feature-based fall indicator variables (FIVs) from the different sensors, and the thresholds for those FIVs are extracted analytically for four major disturbance scenarios. Further, an online threshold interpolation technique and an impulse adaptive counter limit are proposed to manage more generic disturbances. For the generalized prediction process, both the instantaneous and cumulative sum of each FIVs are normalized, and a suitable value is set as the critical limit to predict the fall over. To determine the best combination and the usefulness of multiple sensors, the prediction performance is evaluated on four different types of terrains, in three unique combinations: first, each feature individually with their respective FIVs; second, an intuitive performance based (PF); and finally, Kalman filter based (KF) techniques, which involve the usage of multiple features. For PF and KF techniques, prediction performance evaluations are carried out with and without adding noise. Overall, it is reported that KF performs better than PF and individual sensor features under different conditions. Also, the method\u2019s ability to predict fall overs during the robot\u2019s simple dynamic motion is also tested and verified through simulations. Experimental verification of the proposed prediction method on flat and uneven terrains was carried out with the WALK-MAN humanoid robot. Finally, in reference to the second sub-issue, i.e., the controlled fall, we propose two novel fall control techniques based on energy concepts, which can be applied online to mitigate the impact forces incurred during the falling over of humanoids. Both the techniques are inspired by the break-fall motions, in particular, Ukemi motion practiced by martial arts people. The first technique reduces the total energy using a nonlinear control tool, called energy shaping (ES) and further distributes the reduced energy over multiple contacts by means of energy distribution polygons (EDP). We also include an effective orientation control to safeguard the end-effectors in the event of ground impacts. The performance of the proposed method is numerically evaluated by dynamic simulations under the sudden falling over scenario of the humanoid robot for both lateral and sagittal falls. The effectiveness of the proposed ES and EDP concepts are verified by diverse comparative simulations regarding total energy, distribution, and impact forces. Following the first technique, we proposed another controller to generate an online rolling over motion based on the hypothesis that multi-contact motions can reduce the impact forces even further. To generate efficient rolling motion, critical parameters are defined by the insights drawn from a study on rolling, which are contact positions and attack angles. In addition, energy-injection velocity is proposed as an auxiliary rolling parameter to ensure sequential multiple contacts in rolling. An online rolling controller is synthesized to compute the optimal values of the rolling parameters. The first two parameters are to construct a polyhedron, by selecting suitable contacts around the humanoid\u2019s body. This polyhedron distributes the energy gradually across multiple contacts, thus called energy distribution polyhedron. The last parameter is to inject some additional energy into the system during the fall, to overcome energy drought and tip over successive contacts. The proposed controller, incorporated with energy injection, minimization, and distribution techniques result in a rolling like motion and significantly reduces the impact forces, and it is verified in numerical experiments with a segmented planar robot and a full humanoid model

    Fall Prediction for Bipedal Robots: The Standing Phase

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    This paper presents a novel approach to fall prediction for bipedal robots, specifically targeting the detection of potential falls while standing caused by abrupt, incipient, and intermittent faults. Leveraging a 1D convolutional neural network (CNN), our method aims to maximize lead time for fall prediction while minimizing false positive rates. The proposed algorithm uniquely integrates the detection of various fault types and estimates the lead time for potential falls. Our contributions include the development of an algorithm capable of detecting abrupt, incipient, and intermittent faults in full-sized robots, its implementation using both simulation and hardware data for a humanoid robot, and a method for estimating lead time. Evaluation metrics, including false positive rate, lead time, and response time, demonstrate the efficacy of our approach. Particularly, our model achieves impressive lead times and response times across different fault scenarios with a false positive rate of 0. The findings of this study hold significant implications for enhancing the safety and reliability of bipedal robotic systems.Comment: Submitted to ICRA 2024. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

    Experiments in artificial theory of mind: From safety to story-telling

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    © 2018 Winfield. Theory of mind is the term given by philosophers and psychologists for the ability to form a predictive model of self and others. In this paper we focus on synthetic models of theory of mind. We contend firstly that such models-especially when tested experimentally-can provide useful insights into cognition, and secondly that artificial theory of mind can provide intelligent robots with powerful new capabilities, in particular social intelligence for human-robot interaction. This paper advances the hypothesis that simulation-based internal models offer a powerful and realisable, theory-driven basis for artificial theory of mind. Proposed as a computational model of the simulation theory of mind, our simulation-based internal model equips a robot with an internal model of itself and its environment, including other dynamic actors, which can test (i.e., simulate) the robot's next possible actions and hence anticipate the likely consequences of those actions both for itself and others. Although it falls far short of a full artificial theory of mind, our model does allow us to test several interesting scenarios: in some of these a robot equipped with the internal model interacts with other robots without an internal model, but acting as proxy humans; in others two robots each with a simulation-based internal model interact with each other. We outline a series of experiments which each demonstrate some aspect of artificial theory of mind
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