Real World Speech Interaction with a Humanoid Robot on a Layered Robot Behavior Control Architecture

2006 ◽  
Author(s):  
K. Aoyama ◽  
H. Shimomura
Keyword(s):  
Real World ◽  
Humanoid Robot ◽  
Control Architecture ◽  
Behavior Control ◽  
Robot Behavior ◽  
Speech Interaction

Using Rule-Based Concepts as Foundation for Higher-Level Agent Architectures

2011 ◽  
pp. 493-524 ◽  
Author(s):  
Lars Braubach ◽  
Alexander Pokahr ◽  
Adrian Paschke
Keyword(s):  
Real World ◽  
Application Domain ◽  
Behavior Control ◽  
Software Projects ◽  
Rule Based ◽  
Agent Architectures ◽  
Agent Behavior ◽  
Key Characteristics ◽  
Behavior Specification ◽  
Rule Bases

Declarative programming using rules has advantages in certain application domains and has been successfully applied in many real world software projects. Besides building rule-based applications, rule concepts also provide a proven basis for the development of higher-level architectures, which enrich the existing production rule metaphor with further abstractions. One especially interesting application domain for this technology is the behavior specification of autonomous software agents, because rule bases help fulfilling key characteristics of agents such as reactivity and proactivity. This chapter details which motivations promote the usage of rule bases for agent behavior control and what kinds of approaches exist. Concretely, these approaches are in the context of four existing agent architectures (pure rule-based, AOP, Soar, BDI) and their implementations (Rule Responder, Agent-0 and successors, Soar, and Jadex). In particular, this chapter emphasizes in which respect these agent architectures make use of rules and with what mechanisms they extend the base functionality. Finally, the approaches are generalized by summarizing their core assumptions and extension mechanisms and possible further application domains besides agent architectures are presented.

Real-world reinforcement learning for autonomous humanoid robot docking

2012 ◽  
Vol 60 (11) ◽  
pp. 1400-1407 ◽  
Author(s):  
Nicolás Navarro-Guerrero ◽  
Cornelius Weber ◽  
Pascal Schroeter ◽  
Stefan Wermter
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Humanoid Robot ◽  
Autonomous Humanoid

scholarly journals Exploring Non-Expert Robot Programming Through Crowdsourcing

2021 ◽  
Vol 8 ◽  
Author(s):  
Sanne van Waveren ◽  
Elizabeth J. Carter ◽  
Oscar Örnberg ◽  
Iolanda Leite
Keyword(s):  
Data Collection ◽  
Real World ◽  
Visual Programming ◽  
Robot Programming ◽  
Successful Completion ◽  
Online Study ◽  
Pick And Place ◽  
Robot Behavior ◽  
Robot Tasks ◽  
Programming Interface

A longstanding barrier to deploying robots in the real world is the ongoing need to author robot behavior. Remote data collection–particularly crowdsourcing—is increasingly receiving interest. In this paper, we make the argument to scale robot programming to the crowd and present an initial investigation of the feasibility of this proposed method. Using an off-the-shelf visual programming interface, non-experts created simple robot programs for two typical robot tasks (navigation and pick-and-place). Each needed four subtasks with an increasing number of programming statements (if statement, while loop, variables) for successful completion of the programs. Initial findings of an online study (N = 279) indicate that non-experts, after minimal instruction, were able to create simple programs using an off-the-shelf visual programming interface. We discuss our findings and identify future avenues for this line of research.

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