Real World Browsing System using Humanoid Robot Controlled by Multi-Touch Devices

2011 ◽  
Author(s):  
Mitsuharu Matsumoto
Keyword(s):  
Real World ◽  
Humanoid Robot ◽  
Touch Devices

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

Development of life-sized high-power humanoid robot JAXON for real-world use

2015 ◽  
Author(s):  
Kunio Kojima ◽  
Tatsuhi Karasawa ◽  
Toyotaka Kozuki ◽  
Eisoku Kuroiwa ◽  
Sou Yukizaki ◽  
...  
Keyword(s):  
High Power ◽  
Real World ◽  
Humanoid Robot

Re-posing Objects Considering Bipedal Stability Constraints: An Approach for Stability Preservation During Humanoid Manipulation

2020 ◽  
Vol 17 (02) ◽  
pp. 2050005
Author(s):  
Daniel Sánchez ◽  
Weiwei Wan ◽  
Fumio Kanehiro ◽  
Kensuke Harada
Keyword(s):  
Real World ◽  
Humanoid Robot ◽  
Center Of Mass ◽  
Stability Margin ◽  
Object Manipulation ◽  
Upper Body ◽  
Manipulation Planning ◽  
Stability Estimation

This paper presents a balance-centered planner for object re-posing. It uses Center-of-Mass (CoM) constraints to preserve robot stability and provides stable, IK-feasible, and collision-free upper-body poses, allowing the robot to complete dexterous object manipulation tasks with different objects. The technical contributions of the planner are two-fold. First, it evaluates the robot stability margin for each robot pose during manipulation planning to find a stable manipulation motion. Second, it provides an RRT-inspired task-related stability estimation used to compare different bipedal stances. Simulations and real-world experiments are performed with the HRP-5P humanoid robot, the 5th generation of the HRP robot family, to validate the planner and compare different robot stances and approaches for object re-posing. The experiment results suggest that the proposed planner provides robust behavior for the humanoid robot while performing re-posing tasks.

scholarly journals Robot-Mediated Interviews

2020 ◽  
Vol 21 (2) ◽  
pp. 243-267
Author(s):  
Luke Jai Wood ◽  
Kerstin Dautenhahn ◽  
Austen Rainer ◽  
Ben Robins ◽  
Hagen Lehmann ◽  
...  
Keyword(s):  
Primary School ◽  
School Children ◽  
Real World ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Primary School Children ◽  
Educational Psychologist ◽  
Real World Setting ◽  
Real World Applications ◽  
Before And After

Abstract In recent years the possibility of using humanoid robots to perform interviews with children has been explored in a number of studies. This paper details a study in which a potential real-world user trialled a Robot-Mediated Interviewing system with children to establish if this approach could realistically be used in a real-world context. In this study a senior educational psychologist used the humanoid robot Kaspar to interview ten primary school children about a video they had watched prior to the interview. We conducted a pre and post interview with the educational psychologist before and after using the system to establish how the system worked for him and the perceived potential for real-world applications. The educational psychologist successfully used the system to interview the children and believed that principally using a small humanoid robot to interview children could be useful in a real-world setting provided the system was developed further.

Reasoning based on consolidated real world experience acquired by a humanoid robot

2016 ◽  
Vol 17 (2) ◽  
pp. 248-278 ◽  
Author(s):  
Maxime Petit ◽  
Grégoire Pointeau ◽  
Peter Ford Dominey
Keyword(s):  
Problem Solving ◽  
Human Development ◽  
Real World ◽  
Humanoid Robot ◽  
Expert Knowledge ◽  
Proof Of Concept ◽  
Tower Of Hanoi ◽  
Human Interactions ◽  
Reasoning Systems ◽  
The Individual

Abstract The development of reasoning systems exploiting expert knowledge from interactions with humans is a non-trivial problem, particularly when considering how the information can be coded in the knowledge representation. For example, in human development, the acquisition of knowledge at one level requires the consolidation of knowledge from lower levels. How is the accumulated experience structured to allow the individual to apply knowledge to new situations, allowing reasoning and adaptation? We investigate how this can be done automatically by an iCub that interacts with humans to acquire knowledge via demonstration. Once consolidated, this knowledge is used in further acquisitions of experience concerning preconditions and consequences of actions. Finally, this knowledge is translated into rules that allow reasoning and planning for novel problem solving, including a Tower of Hanoi scenario. We thus demonstrate proof of concept for an interaction system that uses knowledge acquired from human interactions to reason about new situations.

scholarly journals Robust Computer Vision Chess Analysis and Interaction with a Humanoid Robot

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 14 ◽  
Author(s):  
Andrew Chen ◽  
Kevin Wang
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Humanoid Robot ◽  
Sensory Input ◽  
Extreme Environments ◽  
Chess Player ◽  
Dynamic Approach ◽  
Perspective Distortion ◽  
Occupancy Detection ◽  
Vision Algorithm

As we move towards improving the skill of computers to play games like chess against humans, the ability to accurately perceive real-world game boards and game states remains a challenge in many cases, hindering the development of game-playing robots. In this paper, we present a computer vision algorithm developed as part of a chess robot project that detects the chess board, squares, and piece positions in relatively unconstrained environments. Dynamically responding to lighting changes in the environment, accounting for perspective distortion, and using accurate detection methodologies results in a simple but robust algorithm that succeeds 100% of the time in standard environments, and 80% of the time in extreme environments with external lighting. The key contributions of this paper are a dynamic approach to the Hough line transform, and a hybrid edge and morphology-based approach for object/occupancy detection, that enable the development of a robot chess player that relies solely on the camera for sensory input.

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