GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning

• URL: http://arxiv.org/abs/2409.06427v1
• Date: Tue, 10 Sep 2024 11:19:13 GMT
• Title: GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning
• Authors: Kento Kawaharazuka, Kei Okada, Masayuki Inaba,
• Abstract summary: Humans can learn the relationship between sensation and motion in their own bodies. Current robots control their bodies by learning the network structure described by humans from their experiences.
• Score: 18.64205729932939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans can autonomously learn the relationship between sensation and motion in their own bodies, estimate and control their own body states, and move while continuously adapting to the current environment. On the other hand, current robots control their bodies by learning the network structure described by humans from their experiences, making certain assumptions on the relationship between sensors and actuators. In addition, the network model does not adapt to changes in the robot's body, the tools that are grasped, or the environment, and there is no unified theory, not only for control but also for state estimation, anomaly detection, simulation, and so on. In this study, we propose a Generalized Multisensory Correlational Model (GeMuCo), in which the robot itself acquires a body schema describing the correlation between sensors and actuators from its own experience, including model structures such as network input/output. The robot adapts to the current environment by updating this body schema model online, estimates and controls its body state, and even performs anomaly detection and simulation. We demonstrate the effectiveness of this method by applying it to tool-use considering changes in grasping state for an axis-driven robot, to joint-muscle mapping learning for a musculoskeletal robot, and to full-body tool manipulation for a low-rigidity plastic-made humanoid.

Related papers

• Learning body models: from humans to humanoids [2.855485723554975]
  Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. Key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. mechanisms of operation of body models in the brain are largely unknown and even less is known about how they are constructed from experience after birth.
  arXiv  Detail & Related papers  (2022-11-06T07:30:01Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Body Gesture Recognition to Control a Social Robot [5.557794184787908]
  We propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body gestures to train our network.
  arXiv  Detail & Related papers  (2022-06-15T13:49:22Z)
• Data-driven emotional body language generation for social robotics [58.88028813371423]
  In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration. We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
  arXiv  Detail & Related papers  (2022-05-02T09:21:39Z)
• Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks [59.098560311521034]
  We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
  arXiv  Detail & Related papers  (2022-03-29T15:03:05Z)
• PheroCom: Decentralised and asynchronous swarm robotics coordination based on virtual pheromone and vibroacoustic communication [0.0]
  This work proposes a model to coordinate swarms of robots based on the virtualisation and control of stigmergic substances. Each robot maintains an independent virtual pheromone map, which is continuously updated with the robot's deposits and pheromone evaporation. Pheromone information propagation is inspired by ants' vibroacoustic communication, which, in turn, is characterised as an indirect communication through a type of gossip protocol.
  arXiv  Detail & Related papers  (2022-02-27T21:22:14Z)
• Controlling the Sense of Agency in Dyadic Robot Interaction: An Active Inference Approach [6.421670116083633]
  We examine dyadic imitative interactions of robots using a variational recurrent neural network model. We examined how regulating the complexity term to minimize free energy during training determines the dynamic characteristics of networks.
  arXiv  Detail & Related papers  (2021-03-03T02:38:09Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)
• Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots [58.720142291102135]
  Spiking Central Pattern Generator generates different locomotion patterns driven by an external stimulus. The locomotion of the end robotic platform (any-legged robot) can be adapted to the terrain by using any sensor as input.
  arXiv  Detail & Related papers  (2021-01-24T12:44:38Z)
• Sensorimotor representation learning for an "active self" in robots: A model survey [10.649413494649293]
  In humans, these capabilities are thought to be related to our ability to perceive our body in space. This paper reviews the developmental processes of underlying mechanisms of these abilities. We propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents.
  arXiv  Detail & Related papers  (2020-11-25T16:31:01Z)
• Learning Predictive Models From Observation and Interaction [137.77887825854768]
  Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works. However, learning a model that captures the dynamics of complex skills represents a major challenge. We propose a method to augment the training set with observational data of other agents, such as humans.
  arXiv  Detail & Related papers  (2019-12-30T01:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.