Emotion expression is essential for human-robot interaction, yet current systems rely on static models that cannot adapt to individual users. We present an online reinforcement learning framework that adapts robot emotional behavior policy during live dialogue using binary human feedback. The system integrates a DeBERTa-v3-base emotion classifier and applies Group Relative Policy Optimization (GRPO) in a human-robot dialogue system. At each dialogue turn, the classifier samples a group of emotion candidates and the selected emotion is passed to a generative model that synthesizes a novel robot emotional behavior. We evaluate the system in three experiments: (1) offline supervised fine-tuning followed by GRPO on synthetic dialogue data, (2) a live GRPO training with a human teacher and (3) a final experiment with human participants. Results indicate that the robot was perceived as responsive and emotionally consistent, with high ratings for personality coherence and contextual appropriateness of emotional behaviors. Results further show that online GRPO with human feedback enables effective real-time emotion adaptation in embodied interaction.

Robots that search for objects on their own must decide what to do when the search fails, and how they signal that failure to a human partner can determine whether the interaction turns into collaboration. Prior work on human–robot interaction has studied nonverbal help-seeking and social gaze separately, but rarely compares gaze against a matched verbal failure signal inside a live, real-time embodied search task. We present an embodied dialogue system, built on the Retico incremental processing framework, where the Cozmo robot searches for a specified object and signals failure either verbally or through gaze towards the human partner. We first compare a rule-based planner against a vision-language model (VLM) planner that reasons over scene semantics, finding that the VLM planner succeeds far more often (87.2% vs. 65.9%). We then test whether gaze can close this gap without adding reasoning capability to the rule-based planner. Adding gaze as the stuck signal raises success to 87.87%, matching the VLM planner, while a verbal stuck signal only raises it slightly (68.75%). Results further show that this effect is largely implicit: participants helped more in the gaze condition without consistently reporting that they felt invited to do so. Our findings suggest that gaze can substitute for reasoning capability a robot lacks, helping it elicit assistance from a human partner.

This demo will showcase updates made to the ‘robot-ready spoken dialogue system’ built on the Retico framework. Updates include new modules, logging and real-time monitoring tools, integrations with the Coppelia Sim virtual robot platfrom, integrations with a benchmark, improved documentation, and pypi environment usage.

A language model that can generate utterances that are appraised as being within a specific age of a young child who is beginning their language learning journey can be useful in scenarios where child-level language is needed, for example in virtual avatars, interactions with individuals who have disabilities, or developmental robotics. In this paper, we focus on an age range that is not represented in prior work: emergent speakers. We use the CHILDES database to train and tune language models of different parameter sizes using a group relative policy optimization reinforcement learning regime. Our goal is to find the most coherent, yet child-like language model while keeping the number of parameters to as few as possible. We evaluate using metrics of coherency, “toddlerality,” and an evaluation using human subjects who interact with two robot platforms. Our experiments show that even small language models (under 1 billion parameters) can be used effectively to generate child-like utterances.