6 accepted papers
For Deep Reinforcement Learning (DRL) models to deliver actual utility, they must function within production environments, which often lack the extensive computational resources of training environments. This requirement for dedicated GPU resources is not economically feasible and can be especially …
This letter proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition tasks as opposed to a common frame-to-frame retrieval problem formulation currently implemented in SOTA place recognition
Deep reinforcement learning (DRL) methods can solve complex continuous control tasks in simulated environments by taking actions based solely on state observations at each decision point. Because of the dynamics involved, individual snapshots of real-world sensor measurements afford only partial sta…
Deep Reinforcement Learning (DRL) faces challenges bridging the sim-to-real gap to enable real-world applications. In contrast to the simulated environments used in conventional DRL training, real-world systems are non-linear and evolve in an asynchronous fashion; sensors and actuators have limited…
Motion is a critical aspect of communication, required to create natural interactions between humans and robots. Robots for the classroom pose several constraints on motion, which make them challenging to design, including maintaining the safety of the child and the robot, responding in a timely fas…
The use of robots, as a social stimulus, provides several advantages over using another animal. In particular, for rat-robot studies, robots can produce social behaviour that is reproducible across trials. In the current work, we outline a framework for rat-robot interaction studies, that consists o…