2 research outputs found
Using a 3DOF Parallel Robot and a Spherical Bat to hit a Ping-Pong Ball
Playing the game of Ping-Pong is a challenge to human abilities since it requires developing skills, such as fast reaction capabilities, precision of movement and high speed mental responses. These processes include the utilization of seven DOF of the human arm, and translational movements through the legs, torso, and other extremities of the body, which are used for developing different game strategies or simply imposing movements that affect the ball such as spinning movements. Computationally, Ping-Pong requires a huge quantity of joints and visual information to be processed and analysed, something which really represents a challenge for a robot. In addition, in order for a robot to develop the task mechanically, it requires a large and dexterous workspace, and good dynamic capacities. Although there are commercial robots that are able to play Ping-Pong, the game is still an open task, where there are problems to be solved and simplified. All robotic Ping-Pong players cited in the bibliography used at least four DOF to hit the ball. In this paper, a spherical bat mounted on a 3-DOF parallel robot is proposed. The spherical bat is used to drive the trajectory of a Ping-Pong ball.Fil: Trasloheros, Alberto. Universidad Aeronáutica de Querétaro; MéxicoFil: Sebastián, José MarÃa. Universidad Politécnica de Madrid; España. Consejo Superior de Investigaciones CientÃficas; EspañaFil: Torrijos, Jesús. Consejo Superior de Investigaciones CientÃficas; España. Universidad Politécnica de Madrid; EspañaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierÃa. Instituto de Automática; ArgentinaFil: Roberti, Flavio. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierÃa. Instituto de Automática; Argentin
Sample-efficient Reinforcement Learning in Robotic Table Tennis
Reinforcement learning (RL) has achieved some impressive recent successes in
various computer games and simulations. Most of these successes are based on
having large numbers of episodes from which the agent can learn. In typical
robotic applications, however, the number of feasible attempts is very limited.
In this paper we present a sample-efficient RL algorithm applied to the example
of a table tennis robot. In table tennis every stroke is different, with
varying placement, speed and spin. An accurate return therefore has to be found
depending on a high-dimensional continuous state space. To make learning in few
trials possible the method is embedded into our robot system. In this way we
can use a one-step environment. The state space depends on the ball at hitting
time (position, velocity, spin) and the action is the racket state
(orientation, velocity) at hitting. An actor-critic based deterministic policy
gradient algorithm was developed for accelerated learning. Our approach
performs competitively both in a simulation and on the real robot in a number
of challenging scenarios. Accurate results are obtained without pre-training in
under episodes of training. The video presenting our experiments is
available at https://youtu.be/uRAtdoL6Wpw.Comment: accepted at ICRA 2021 (Xian, China