One important aspect of effective human--robot collaborations is the ability for robots to
adapt quickly to the needs of humans. While techniques like deep reinforcement learning …

Reinforcement learning (RL) is one of the active fields in machine learning, demonstrating
remarkable potential in tackling real-world challenges. Despite its promising prospects, this …

Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential
decision-making tasks across diverse domains, yet its reliance on black-box neural …

DH Despite significant improvements in robot capabilities, they are likely to fail in human-
robot collaborative tasks due to high unpredictability in human environments and varying …

This paper presents an overview of robot failure detection work from HRI and adjacent fields
using failures as an opportunity to examine robot explanation behaviours. As humanoid …

Robots are well-suited to alleviate the burden of repetitive and tedious manipulation tasks. In
many applications though, a robot may be asked to interact with a wide variety of objects …

In human-robot collaboration, failures are bound to occur. A thorough understanding of
potential errors is necessary so that robotic system designers can develop systems that …

Many tasks require robots to manipulate objects while satisfying a complex interplay of
spatial and temporal constraints. For instance, a table setting robot first needs to place a …

Deep reinforcement learning (DRL) is playing an increasingly important role in real-world
applications. However, obtaining an optimally performing DRL agent for complex tasks …

A crucial part of traditional reinforcement learning (RL) is the initial exploration phase, in
which trying available actions randomly is a critical element. As random behavior might be …