557 research outputs found
DribbleBot: Dynamic Legged Manipulation in the Wild
DribbleBot (Dexterous Ball Manipulation with a Legged Robot) is a legged
robotic system that can dribble a soccer ball under the same real-world
conditions as humans (i.e., in-the-wild). We adopt the paradigm of training
policies in simulation using reinforcement learning and transferring them into
the real world. We overcome critical challenges of accounting for variable ball
motion dynamics on different terrains and perceiving the ball using
body-mounted cameras under the constraints of onboard computing. Our results
provide evidence that current quadruped platforms are well-suited for studying
dynamic whole-body control problems involving simultaneous locomotion and
manipulation directly from sensory observations.Comment: To appear at the IEEE Conference on Robotics and Automation (ICRA),
2023. Video is available at https://gmargo11.github.io/dribblebot
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
This paper describes an approach to the design of a population of cooperative
robots based on concepts borrowed from Systems Theory and Artificial
Intelligence. The research has been developed under the SocRob project, carried
out by the Intelligent Systems Laboratory at the Institute for Systems and
Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the
project stands both for "Society of Robots" and "Soccer Robots", the case study
where we are testing our population of robots. Designing soccer robots is a
very challenging problem, where the robots must act not only to shoot a ball
towards the goal, but also to detect and avoid static (walls, stopped robots)
and dynamic (moving robots) obstacles. Furthermore, they must cooperate to
defeat an opposing team. Our past and current research in soccer robotics
includes cooperative sensor fusion for world modeling, object recognition and
tracking, robot navigation, multi-robot distributed task planning and
coordination, including cooperative reinforcement learning in cooperative and
adversarial environments, and behavior-based architectures for real time task
execution of cooperating robot teams
Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions
This paper addresses the design and implementation of complex Reinforcement
Learning (RL) behaviors where multi-dimensional action spaces are involved, as
well as the need to execute the behaviors in real-time using robotic platforms
with limited computational resources and training times. For this purpose, we
propose the use of decentralized RL, in combination with finite support basis
functions as alternatives to Gaussian RBF, in order to alleviate the effects of
the curse of dimensionality on the action and state spaces respectively, and to
reduce the computation time. As testbed, a RL based controller for the in-walk
kick in NAO robots, a challenging and critical problem for soccer robotics, is
used. The reported experiments show empirically that our solution saves up to
99.94% of execution time and 98.82% of memory consumption during execution,
without diminishing performance compared to classical approaches.Comment: Accepted in the RoboCup Symposium 2017. Final version will be
published at Springe
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