Adaptive Robot Body Learning and Estimation Through Predictive Coding

Abstract

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.

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

“…We formalized the rubber hand illusion as a body estimation problem under the predictive processing framework. The core idea behind this is that all features and sensory modalities are contributing to refine body estimation through the minimization of the errors between sensations and predictions [27]. During synchronous visuo-tactile stimulation, the most plausible body configuration is perturbed due to the merging of visual and proprioceptive information.…”

“…By rewriting the prediction error as e = s − g(x) and defining µ x as the prior belief about the body configuration, the dynamics of the body perception model are described by (see Appendix for derivation and [27] for the detailed algorithm):ẋ…”

An interdisciplinary overview of developmental indices and behavioral measures of the minimal self

“…Using a multisensory robotic arm, Hinz et al [51] replicated these drifting patterns in both human and robot experiments with the classic ("passive") RHI paradigm. The learning and estimation algorithm [52] used in the study was based on the framework of predictive coding [21]. Specifically, Lanillos and Cheng [52] developed a method for integrating different sources of information (tactile, visual, and proprioceptive) that drives the robot priors to infer its body configuration.…”

“…The learning and estimation algorithm [52] used in the study was based on the framework of predictive coding [21]. Specifically, Lanillos and Cheng [52] developed a method for integrating different sources of information (tactile, visual, and proprioceptive) that drives the robot priors to infer its body configuration. This computational perceptual model enables a multisensory robot to learn, make inferences, and update its body configuration from its sensors.…”

“…They modeled the robot body estimation as a process of minimizing the prediction error between the body configuration "belief" (prediction) and the observed posterior, and minimizing the variational free energy [21] by using the sensory prediction error. Using the algorithm in [52], Hinz et al [51] showed that body configuration estimation can be done through minimization of prediction error as one process that involves both predictive coding and causal inference. The results from the human and robot experiments suggest that the perceived locations of both the real and the dummy hand drift to a common location between them.…”