Yielding Self-Perception in Robots Through Sensorimotor Contingencies

Lanillos, Pablo; Dean-León, Emmanuel; Cheng, Gordon

doi:10.1109/tcds.2016.2627820

Cited by 35 publications

(34 citation statements)

References 41 publications

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“…We replicated the passive rubber hand illusion on a multisensory robot and compared it with human participants, therefore gaining insight into the perceptive contribution to selfcomputation. Enabling a robot with human-like self-perception [3] is important for: i) improving the machine adaptability This work has been supported by SELFCEPTION project (www.selfception.eu) European Union Horizon 2020 Programme (MSCA-IF-2016) under grant agreement n. 741941 and the ENB Master Program in Neuro-Cognitive Psychology at Ludwig-Maximilians Universität. Video to this paper: http://web.ics.ei.tum.de/~pablo/rubberICDL2018PL.mp4…”

Section: Introductionmentioning

confidence: 99%

Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot

Hinz

Lanillos

Mueller

et al. 2018

2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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Humans can experience fake body parts as theirs just by simple visuo-tactile synchronous stimulation. This bodyillusion is accompanied by a drift in the perception of the real limb towards the fake limb, suggesting an update of body estimation resulting from stimulation. This work compares body limb drifting patterns of human participants, in a rubber hand illusion experiment, with the end-effector estimation displacement of a multisensory robotic arm enabled with predictive processing perception. Results show similar drifting patterns in both human and robot experiments, and they also suggest that the perceptual drift is due to prediction error fusion, rather than hypothesis selection. We present body inference through prediction error minimization as one single process that unites predictive coding and causal inference and that it is responsible for the effects in perception when we are subjected to intermodal sensory perturbations.

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Section: Introductionmentioning

confidence: 99%

Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot

Hinz

Lanillos

Mueller

et al. 2018

2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

Self Cite

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“…We exploit the artificial skin of the robot to refine the body-configuration estimation. For that purpose, we model the intermodal relation between visual and the tactile sensing [4]. When somebody touches the robot end-effector, it should adjust its body configuration to fit the end-effector location in the visual field where the other agent is touching.…”

Section: Adding Tactile Feedback and Other's Interactionmentioning

confidence: 99%

“…The main idea behind this embodied approach of robot body perception is that the only available information is the sensory input [4]. By learning the predictors of the sensor outcome given its current body latent variables and the actions exerted, the robot is able to properly infer its real body configuration.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robot Body Learning and Estimation Through Predictive Coding

Lanillos

Cheng

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The predictive functions that permit humans to infer their body state by sensorimotor integration are critical to perform safe interaction in complex environments. These functions are adaptive and robust to non-linear actuators and noisy sensory information. This paper introduces a computational perceptual model based on predictive processing that enables any multisensory robot to learn, infer and update its body configuration when using arbitrary sensors with Gaussian additive noise. The proposed method integrates different sources of information (tactile, visual and proprioceptive) to drive the robot belief to its current body configuration. The motivation is to enable robots with the embodied perception needed for self-calibration and safe physical human-robot interaction.We formulate body learning as obtaining the forward model that encodes the sensor values depending on the body variables, and we solve it by Gaussian process regression. We model body estimation as minimizing the discrepancy between the robot body configuration belief and the observed posterior. We minimize the variational free energy using the sensory prediction errors (sensed vs expected).In order to evaluate the model we test it on a real multisensory robotic arm. We show how different sensor modalities contributions, included as additive errors, improve the refinement of the body estimation and how the system adapts itself to provide the most plausible solution even when injecting strong sensory visuo-tactile perturbations. We further analyse the reliability of the model when different sensor modalities are disabled. This provides grounded evidence about the correctness of the perceptual model and shows how the robot estimates and adjusts its body configuration just by means of sensory information.Index Terms-Bio-inspired perception, body-schema, predictive processing, embodied artificial intelligence, learning and adaptive systems, humanoid robotics.

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“…Sensorimotor learning involves neural mapping between the motor and sensory variables, sensorymotor processing, sensory-motor transformations, and their modifications [100], which represent internal models. Significant advances have been made with applications to robotics [101,102].…”

Section: Reinforcement Learningmentioning

confidence: 99%

A Review of the Relationship between Novelty, Intrinsic Motivation and Reinforcement Learning

Siddique

Dhakan

Rañó

et al. 2017

Paladyn, Journal of Behavioral Robotics

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This paper presents a review on the tri-partite relationship between novelty, intrinsic motivation and reinforcement learning. The paper first presents a literature survey on novelty and the different computational models of novelty detection, with a specific focus on the features of stimuli that trigger a Hedonic value for generating a novelty signal. It then presents an overview of intrinsic motivation and investigations into different models with the aim of exploring deeper co-relationships between specific features of a novelty signal and its effect on intrinsic motivation in producing a reward function. Finally, it presents survey results on reinforcement learning, different models and their functional relationship with intrinsic motivation.

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Yielding Self-Perception in Robots Through Sensorimotor Contingencies

Cited by 35 publications

References 41 publications

Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot

Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot

Adaptive Robot Body Learning and Estimation Through Predictive Coding

A Review of the Relationship between Novelty, Intrinsic Motivation and Reinforcement Learning

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