Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Metasensor: a proposal for sensor evolution in robotics

Michele Braccini

TL;DR

Metasensor proposes an architectural layer that enables online sensor evolution for robots by separating interpretation from control, expanding perceptual capacity without altering existing controllers. It formalizes a model-free search over perceptual processing and memory-enabled interpretation, with a hardware interface extending inputs from $m$ to $n$ and a software module that continuously adapts to task and environment. By situating this mechanism within cybernetics and biosemiotics, the work introduces a signification-driven approach to robotic perception that can yield robust behavior in uncertain settings. The proposed metasensor offers practical potential to improve fault tolerance and adaptability while reducing the need for redesigns of sensing hardware or control software.

Abstract

Sensors play a fundamental role in achieving the complex behaviors typically found in biological organisms. However, their potential role in the design of artificial agents is often overlooked. This often results in the design of robots that are poorly adapted to the environment, compared to their biological counterparts. This paper proposes a formalization of a novel architectural component, called a metasensor, which enables a process of sensor evolution reminiscent of what occurs in living organisms. Even in online scenarios, the metasensor layer searches for the optimal interpretation of its input signals and then feeds them to the robotic agent to accomplish the assigned task.

Metasensor: a proposal for sensor evolution in robotics

TL;DR

Metasensor proposes an architectural layer that enables online sensor evolution for robots by separating interpretation from control, expanding perceptual capacity without altering existing controllers. It formalizes a model-free search over perceptual processing and memory-enabled interpretation, with a hardware interface extending inputs from to and a software module that continuously adapts to task and environment. By situating this mechanism within cybernetics and biosemiotics, the work introduces a signification-driven approach to robotic perception that can yield robust behavior in uncertain settings. The proposed metasensor offers practical potential to improve fault tolerance and adaptability while reducing the need for redesigns of sensing hardware or control software.

Abstract

Sensors play a fundamental role in achieving the complex behaviors typically found in biological organisms. However, their potential role in the design of artificial agents is often overlooked. This often results in the design of robots that are poorly adapted to the environment, compared to their biological counterparts. This paper proposes a formalization of a novel architectural component, called a metasensor, which enables a process of sensor evolution reminiscent of what occurs in living organisms. Even in online scenarios, the metasensor layer searches for the optimal interpretation of its input signals and then feeds them to the robotic agent to accomplish the assigned task.
Paper Structure (4 sections, 2 equations, 1 figure)

This paper contains 4 sections, 2 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Metasensor model: control-by-interpretation
  3. Between Cybernetics and (Bio)Semiotics
  4. Conclusion

Figures (1)

  • Figure 1: Role of metasensor in the robot sensorimotor loop. (a) Abstract representation of the components of the sensorimotor loop of a robotic agent. (b) Schematic representation of a robot with a sensorimotor loop composed of two sensors (S) two actuators (A) and a control software (C). (c) Diagram representing the role of the metasensor in the robot sensorimotor loop: in short, it enhances the sensory capabilities of the robot by increasing the number and type of sensors and offering memory-based processing. (d) Possible instantiation of the metasensor concept with the robot proposed in (b): an example of the internal system that composes the metasensor processing unit in the form of a recurrent network is illustrated.