scholarly journals An End-to-End System for Accomplishing Tasks with Modular Robots: Perspectives for the AI community

2017 ◽  
Author(s):  
Gangyuan Jing ◽  
Tarik Tosun ◽  
Mark Yim ◽  
Hadas Kress-Gazit
Keyword(s):  
Modular Robots ◽  
Simulation Tool ◽  
Modular Robot ◽  
Broad Audience ◽  
Robot Systems ◽  
End To End ◽  
And Behaviors ◽  
High Level

The advantage of modular robot systems lies in their flexibility, but this advantage can only be realized if there exists some reliable, effective way of generating configurations (shapes) and behaviors (controlling programs) appropriate for a given task. In this paper, we present an end-to-end system for addressing tasks with modular robots, and demonstrate that it is capable of accomplishing challenging multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) A high-level mission planner, (2) A design library spanning a wide set of functionality, (3) A design and simulation tool for populating the library with new configurations and behaviors, and (4) Modular robot hardware. This paper condenses the material originally presented in Jing et al. 2016 into a shorter format suitable for a broad audience.

Bio-Inspired Coalition Formation Algorithms for Multirobot Systems

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Binsen Qian ◽  
Harry H. Cheng
Keyword(s):  
Mathematical Model ◽  
Coalition Formation ◽  
Ant Colony Algorithm ◽  
Multirobot Systems ◽  
Modular Robots ◽  
Modular Robot ◽  
Parallel Performance ◽  
Robot Systems ◽  
Efficiency And Effectiveness ◽  
Simulation And Experiment

This paper presents two bio-inspired algorithms for coalition formation of multiple modular robot systems. An effective and efficient coalition formation system can help modular robot system take full advantage of reconfigurability of modular robots. In this paper, the multirobot coalition formation problem is illustrated and a mathematical model for the problem is described. Two bio-inspired algorithms, ant-colony algorithm (ACA) and genetic algorithm (GA), are introduced for solving the mathematical model. With the two algorithms, it is able to form a large number of robots into many different groups for a variety of applications, such as parallel performance of multiple tasks by multiple teams of robots. The paper compares the efficiency and effectiveness of two algorithms for solving the presented problem with case study. The results for the comparison study are analyzed and discussed. Also, the implementation details of the simulation and experiment using ACA are presented in the paper.

scholarly journals An integrated system for perception-driven autonomy with modular robots

2018 ◽  
Vol 3 (23) ◽  
pp. eaat4983 ◽  
Author(s):  
Jonathan Daudelin ◽  
Gangyuan Jing ◽  
Tarik Tosun ◽  
Mark Yim ◽  
Hadas Kress-Gazit ◽  
...  
Keyword(s):  
A Priori ◽  
Integrated System ◽  
Modular Robots ◽  
Modular Robot ◽  
Robot System ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Task Specification ◽  
Mechanical Elements ◽  
High Level

The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field. We present a modular robot system capable of autonomously completing high-level tasks by reactively reconfiguring to meet the needs of a perceived, a priori unknown environment. The system integrates perception, high-level planning, and modular hardware and is validated in three hardware demonstrations. Given a high-level task specification, a modular robot autonomously explores an unknown environment, decides when and how to reconfigure, and manipulates objects to complete its task. The system architecture balances distributed mechanical elements with centralized perception, planning, and control. By providing an example of how a modular robot system can be designed to leverage reactive reconfigurability in unknown environments, we have begun to lay the groundwork for modular self-reconfigurable robots to address tasks in the real world.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

EeteS—The EnMAP End-to-End Simulation Tool

2012 ◽  
Vol 5 (2) ◽  
pp. 522-530 ◽  
Author(s):  
Karl Segl ◽  
Luis Guanter ◽  
Christian Rogass ◽  
Theres Kuester ◽  
Sigrid Roessner ◽  
...  
Keyword(s):  
Simulation Tool ◽  
End To End

Modular Robot Connection Design

1992 ◽  
Author(s):  
Robert O. Ambrose ◽  
Delbert Tesar
Keyword(s):  
Product Line ◽  
Modular Robots ◽  
Modular Robotics ◽  
Modular Robot ◽  
Manufacturing Costs ◽  
Mechanical Connection ◽  
Automation Equipment ◽  
Connection Design

Abstract The ability to reconfigure automation equipment will reduce the manufacturing costs of obsolesence, training and maintenance while allowing for a faster response to changes in the product line. A modular philosophy will give the user these advantages, but only if based on a common connection standard. A mechanical connection was selected for the UT Modular Robotics Testbed and used in the designs of four robot joint modules and nine robot link modules. The standard was also used for assecories, such as the testand, loading fixtures and endeffectors. Three years of experiments with this connection standard are reviewed, and used as the basis for new connection designs. Experiments using multiple modules assembled as dextrous robots, as well as experiments focusing on the connection itself, will be described. Goals for future connection standards include designs with upward compatibility, combinations of both mechanical and electrical fittings, and robot triendly constraints that allow for automated or remote assembly of modular robots.

The robot-to-robot service encounter: an examination of the impact of inter-robot warmth

2021 ◽  
Vol 35 (9) ◽  
pp. 15-27
Author(s):  
Magnus Söderlund
Keyword(s):  
Design Methodology ◽  
Service Robots ◽  
Service Robot ◽  
Service Encounters ◽  
Experimental Setting ◽  
Content Type ◽  
Robot Systems ◽  
Synthetic Agents ◽  
High Level ◽  
The Impact

Purpose This study aims to examine humans’ reactions to service robots’ display of warmth in robot-to-robot interactions – a setting in which humans’ impressions of a service robot will not only be based on what this robot does in relation to humans, but also on what it does to other robots. Design/methodology/approach Service robot display of warmth was manipulated in an experimental setting in such a way that a service robot A expressed low versus high levels of warmth in relation to another service robot B. Findings The results indicate that a high level of warmth expressed by robot A vis-à-vis robot B boosted humans’ overall evaluations of A, and that this influence was mediated by the perceived humanness and the perceived happiness of A. Originality/value Numerous studies have examined humans’ reactions when they interact with a service robot or other synthetic agents that provide service. Future service encounters, however, will comprise also multi-robot systems, which means that there will be many opportunities for humans to be exposed to robot-to-robot interactions. Yet, this setting has hitherto rarely been examined in the service literature.

scholarly journals Solving Probability Problems in Natural Language

2017 ◽  
Author(s):  
Anton Dries ◽  
Angelika Kimmig ◽  
Jesse Davis ◽  
Vaishak Belle ◽  
Luc de Raedt
Keyword(s):  
Natural Language ◽  
Discrete Mathematics ◽  
Second Step ◽  
Programming System ◽  
Mathematics Textbooks ◽  
Intellectual Skill ◽  
Correct Model ◽  
Level Model ◽  
End To End ◽  
High Level

The ability to solve probability word problems such as those found in introductory discrete mathematics textbooks, is an important cognitive and intellectual skill. In this paper, we develop a two-step end-to-end fully automated approach for solving such questions that is able to automatically provide answers to exercises about probability formulated in natural language.In the first step, a question formulated in natural language is analysed and transformed into a high-level model specified in a declarative language. In the second step, a solution to the high-level model is computed using a probabilistic programming system. On a dataset of 2160 probability problems, our solver is able to correctly answer 97.5% of the questions given a correct model. On the end-to-end evaluation, we are able to answer 12.5% of the questions (or 31.1% if we exclude examples not supported by design).

scholarly journals Towards High-Level Intrinsic Exploration in Reinforcement Learning

2020 ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
State Of The Art ◽  
Experimental Results ◽  
Prior Work ◽  
Extrinsic Rewards ◽  
Intrinsic Reward ◽  
Long Time ◽  
End To End ◽  
High Level

Deep reinforcement learning (DRL) methods traditionally struggle with tasks where environment rewards are sparse or delayed, which entails that exploration remains one of the key challenges of DRL. Instead of solely relying on extrinsic rewards, many state-of-the-art methods use intrinsic curiosity as exploration signal. While they hold promise of better local exploration, discovering global exploration strategies is beyond the reach of current methods. We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning. Our curiosity signal is driven by a fast reward that deals with local exploration and a slow reward that incentivizes long-time horizon exploration strategies. We formulate curiosity as the error in an agent’s ability to reconstruct the observations given their contexts. Experimental results show that this high-level exploration enables our agents to outperform prior work in several Atari games.

scholarly journals Accomplishing high-level tasks with modular robots

2018 ◽  
Vol 42 (7) ◽  
pp. 1337-1354 ◽  
Author(s):  
Gangyuan Jing ◽  
Tarik Tosun ◽  
Mark Yim ◽  
Hadas Kress-Gazit
Keyword(s):  
Modular Robots ◽  
High Level
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