The field of human-robot interaction and collaboration is moving towards more sophisticated and dynamic models of interaction, with a focus on advancing the ability of robots to understand and respond to human needs and behaviors. This includes the development of new datasets and benchmarks for human-object interaction, human-human interaction, and human-robot collaboration, which are enabling more accurate and effective models of motion prediction and scene understanding. Additionally, there is a growing emphasis on the importance of transparency, trust, and safety in human-robot interaction, with new architectures and frameworks being proposed to address these challenges. Notable papers in this area include: HHI-Assist, which introduces a new dataset and benchmark for human-human interaction in physical assistance scenarios, and demonstrates improved motion prediction and generalization to unseen scenarios. HARMONIC, which presents a cognitive-robotic architecture designed for robots in human-robotic teams, and promotes transparency and trust. Human Interaction for Collaborative Semantic SLAM, which introduces a human-in-the-loop semantic SLAM framework that uses extended reality for real-time collaboration and improves room detection accuracy and map precision. Affordance-Based Disambiguation of Surgical Instructions, which presents a framework for a robotic surgical assistant that interprets and disambiguates verbal instructions from a surgeon using a two-level affordance-based reasoning process.