Towards a Swarm of Robots for Detecting Breaches in Social Distancing

Robotic agents can continuously provide feedback to people based on their behaviors. For instance, a robot swarm can remind a group of people to respect social distancing guidelines during a pandemic or discourage unwanted behavior such as littering. However, developing a swarm robot to operate in realistic situations is challenging: a robot...

Sample effficient deep reinforcement learning for control

The arrival of intelligent, general-purpose robots that can learn to perform new tasks autonomously has been promised for a long time now. Deep reinforcement learning, which combines reinforcement learning with deep neural network function approximation, has the potential to enable robots to learn to perform a wide range of new tasks while...

Reinforcement learning based compensation methods for robot manipulators

Smart robotics will be a core feature while migrating from Industry 3.0 (i.e., mass manufacturing) to Industry 4.0 (i.e., customized or social manufacturing). A key characteristic of a smart system is its ability to learn. For smart manufacturing, this means incorporating learning capabilities into the current fixed, repetitive, task-oriented...

Robotic Auxiliary Losses for continuous reinforcement learning

Recent advancements in computation power and artificial intelligence have allowed the creation of advanced reinforcement learning models which could revolutionize, between others, the field of robotics. As model and environment complexity increase, however, training solely through the feedback of environment reward becomes more difficult. From...

Experience selection in deep reinforcement learning for control

Experience replay is a technique that allows off-policy reinforcement-learning methods to reuse past experiences. The stability and speed of convergence of reinforcement learning, as well as the eventual performance of the learned policy, are strongly dependent on the experiences being replayed. Which experiences are replayed depends on two...

Directed Increment Policy Search for Behavior Tree Task Performance Optimization: Crossing the Reality Gap

Robotic behavior policies learned in simulation suffer from a performance degradation once transferred to a real-world robotic platform. This performance degradation originates from discrepancies between the real-world and simulation environment, referred to as the reality gap. To cross the reality gap, this papers presents a simple...

Automatic synthesis of supervisory control systems

Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. Sequential composition focuses on the interaction between a collection of pre-designed...