2016 - ACM Fellow For contributions to the field of artificial intelligence, in particular in planning, learning, multi-agent systems, and robotics.
2011 - IEEE Fellow For contributions to the development of cognition, perception, and action in autonomous robot teams
2010 - Fellow of the American Association for the Advancement of Science (AAAS)
2003 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the development of planning and learning algorithms, and multiagent robot teams for uncertain dynamic environments.
Manuela Veloso mostly deals with Artificial intelligence, Robot, Domain, Human–computer interaction and Mobile robot. Her biological study spans a wide range of topics, including Machine learning, Task and Computer vision. Her Machine learning study combines topics from a wide range of disciplines, such as Structure, Probabilistic logic and Feature extraction.
As a part of the same scientific study, Manuela Veloso usually deals with the Robot, concentrating on Asynchronous communication and frequently concerns with Planner. Her work carried out in the field of Domain brings together such families of science as Teamwork, Representation, Adaptation and Set. Her Mobile robot research incorporates themes from Domain knowledge and Motion planning.
Her primary areas of investigation include Artificial intelligence, Robot, Human–computer interaction, Mobile robot and Domain. Her Artificial intelligence research incorporates elements of Machine learning and Computer vision. She works in the field of Computer vision, focusing on Object in particular.
As part of her studies on Robot, she often connects relevant subjects like Task. She interconnects Variety, Human–robot interaction and Set in the investigation of issues within Human–computer interaction. Domain is closely attributed to Plan in her study.
Her primary areas of study are Robot, Artificial intelligence, Human–computer interaction, Task and Reinforcement learning. Robot is closely attributed to Set in her research. She combines subjects such as Machine learning and Computer vision with her study of Artificial intelligence.
Her Human–computer interaction research integrates issues from Human–robot interaction, Service robot, Object, Mobile service and Autonomous robot. The Reinforcement learning study combines topics in areas such as Control and Markov decision process. The various areas that Manuela Veloso examines in her Humanoid robot study include Stackelberg competition and Autonomous agent.
Manuela Veloso spends much of her time researching Robot, Artificial intelligence, Human–computer interaction, Mobile robot and Computer vision. Manuela Veloso has included themes like Motion, Task and Heuristic in her Robot study. Her Machine learning research extends to Artificial intelligence, which is thematically connected.
The study incorporates disciplines such as Human–robot interaction, Object, Mobile service, Autonomous robot and Robustness in addition to Human–computer interaction. Her work on Mobile robot navigation as part of general Mobile robot study is frequently linked to Active sensing, therefore connecting diverse disciplines of science. Her study in Computer vision is interdisciplinary in nature, drawing from both Animation and Trajectory.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A survey of robot learning from demonstration
Brenna D. Argall;Sonia Chernova;Manuela Veloso;Brett Browning.
Robotics and Autonomous Systems (2009)
Multiagent Systems: A Survey from a Machine Learning Perspective
Peter Stone;Manuela Veloso.
Autonomous Robots (2000)
SPIRAL: Code Generation for DSP Transforms
M. Puschel;J.M.F. Moura;J.R. Johnson;D. Padua.
Proceedings of the IEEE (2005)
Multiagent Learning Using a Variable Learning Rate
Michael H. Bowling;Manuela M. Veloso.
Artificial Intelligence (2002)
Fast and inexpensive color image segmentation for interactive robots
J. Bruce;T. Balch;M. Veloso.
intelligent robots and systems (2000)
The grand challenges of Science Robotics
Guang Zhong Yang;Jim Bellingham;Pierre E. Dupont;Peer Fischer;Peer Fischer.
Science Robotics (2018)
Peter Stone;Manuela M. Veloso.
european conference on machine learning (2000)
Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork
Peter Stone;Manuela Veloso.
Artificial Intelligence (1999)
Integrating planning and learning: the PRODIGY architecture
Manuela M. Veloso;Jaime G. Carbonell;M. Alicia Pérez;Daniel Borrajo.
Journal of Experimental and Theoretical Artificial Intelligence (1995)
The RoboCup Synthetic Agent Challenge 97
Hiroaki Kitano;Milind Tambe;Peter Stone;Manuela M. Veloso.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: