4 accepted papers
Learning to manipulate objects efficiently, particularly those involving sustained contact (e.g., pushing, sliding) and articulated parts (e.g., drawers, doors), presents significant challenges. Traditional methods, such as robot-centric reinforce-ment learning (RL), imitation learning, and hybrid t
Practical and ethical dataset collection remains a challenge blocking many empirical methods in natural language processing, resulting in a lack of benchmarks or data on which to test hypotheses. We propose a solution to some of these areas by presenting a pipeline to reduce the research burden of p…
Reinforcement learning techniques are widely used when robots have to learn new tasks but they typically operate on action spaces defined by the joints of the robot. We present a contrasting approach where actions spaces are the trajectories of objects in the environment, requiring robots to discove…
Regular irradiation of indoor environments with ultraviolet C (UVC) light has become a regular task for many in-door settings as a result of COVID-19, but current robotic systems attempting to automate it suffer from high costs and inefficient irradiation. In this paper, we propose a purpose-made in…