Elizabeth A. Croft

Communication is essential for successful interaction. In Human–Robot Interaction (HRI), implicit communication holds the potential to enhance robots’ understanding of human needs, emotions and intentions. This article introduces a method to foster implicit communication in HRI without explicitly modelling human intentions or relying on pre-existing knowledge. Leveraging Transfer Entropy, we modulate influence between agents in social interactions in scenarios involving either collaboration or competition. By integrating influence into agents’ rewards within a partially observable Markov decision process, we demonstrate that boosting influence enhances collaboration and interaction, while resisting influence promotes social independence and diminishes performance in certain scenarios. Our findings are validated through simulations and real-world experiments with human participants in social navigation and autonomous driving settings.

Nonverbal communication through expressive robot motion significantly enhances human-robot interaction (HRI), yet designing motions that are both individually legible and collectively distinguishable remains challenging. Existing large language model (LLM)-based methods can successfully generate expressive robot motions but can fail to adequately differentiate motions for communicating closely related functional robot states, resulting in perceptual confusion. To address this, we introduce a hybrid optimization framework combining an LLM-derived accuracy proxy with distance metrics to autonomously produce legible and distinct expressive robot motions. We explore two distance metrics: the Earth Mover’s Distance (EMD) and a kinematic trajectory distance. A validation experiment demonstrates the LLM-generated accuracy proxy’s capability to mimic human perceptual responses (90.4% alignment), offering a scalable method for evaluating expressive robot motions without human observers. Results from a user study ($N=79$) demonstrate that the proposed optimization method significantly enhanced state classification accuracy over LLM-generated motions. The inclusion of the kinematic distance metric provided the greatest improvement in legibility. The trade-off between motion clarity and distance between motions emerged as a critical design parameter.

Learning from Demonstration (LfD) empowers robots to acquire new skills through human demonstrations, making it feasible for everyday users to teach robots. However, the success of learning and generalization heavily depends on the quality of these demonstrations. Consistency is often used to indicate quality in LfD, yet the factors that define this consistency remain underexplored. In this article, we evaluate a comprehensive set of motion data characteristics to determine which consistency measures best predict learning performance. By ensuring demonstration consistency prior to training, we enhance models’ predictive accuracy and generalization to novel scenarios. We validate our approach with two user studies involving participants with diverse levels of robotics expertise. In the first study ( N  =   24), users taught a PR2 robot to perform a button-pressing task in a constrained environment, while in the second study ( N  =   30), participants trained an UR5 robot on a pick-and-place task. Results show that demonstration consistency significantly impacts success rates in both learning and generalization, with 70% and 89% of task success rates in the two studies predicted using our consistency metrics. Moreover, our metrics estimate generalized performance success rates with 76% and 91% accuracy. These findings suggest that our proposed measures provide an intuitive, practical way to assess demonstration data quality before training, without requiring expert data or algorithm-specific modifications. Our approach offers a systematic way to evaluate demonstration quality, addressing a critical gap in LfD by formalizing consistency metrics that enhance the reliability of robot learning from human demonstrations.

Learning from Demonstration (LfD) is a powerful type of machine learning that can allow novices to teach robots to complete various tasks. However, the learning process for these systems may still be difficult for novices to interpret and understand, making effective teaching challenging. Explainable artificial intelligence (XAI) aims to address this challenge by explaining a system to the user. In this work, we investigate XAI within LfD by implementing an adaptive explanatory feedback system on an inverse reinforcement learning (IRL) algorithm. The feedback is implemented by demonstrating selected learnt trajectories to users. The system adapts to user teaching by categorizing and then selectively sampling trajectories shown to a user, to show a representative sample of both successful and unsuccessful trajectories. The system was evaluated through a user study with 26 participants teaching a robot a multi-goal navigation task. The results of the user study demonstrated that the proposed explanatory feedback system can improve robot performance, teaching efficiency and the user's ability to predict the robot's goals and actions.

When humans work together to complete a joint task, each person builds an internal model of the situation and how it will evolve. Efficient collaboration depends on how these individual models overlap to form a shared mental model among team members; shared models are also important for collaborative processes in human–robot teams. The development and maintenance of an accurate shared mental model requires bidirectional communication of individual intent and the ability to interpret the intent of other team members. To enable effective human–robot collaboration, this article investigates the use of augmented reality (AR) technology and user eye gaze to enable bidirectional communication of intent in a joint action task. We tested this approach through a user study with 37 participants and found that this communication improves task efficiency, trust, as well as task fluency. We conclude that using AR and eye gaze to enable bidirectional communication and support shared mental models is a promising means for improving collaboration between humans and robots.

Expressive robot motion is a form of nonverbal communication that enables robots to convey their internal states, fostering effective human-robot interaction. A key step in designing expressive robot motions is developing a mapping from the desired states the robot will express to the robot's hardware and available degrees of freedom (design space). This letter introduces a novel framework to autonomously generate this mapping by leveraging a large language model (LLM) to select motion parameters and their values for target robot states. We evaluate expressive robot body language displayed on a Unitree Go1 quadruped as generated by a Generative Pre-trained Transformer (GPT) provided with a set of adjustable motion parameters. Through a two-part study (N = 120), we compared LLM-generated expressive motions with both randomly selected and human-selected expressions. Our results show that participants viewing LLM-generated expressions achieve a significantly higher state classification accuracy over random baselines and perform comparably with human-generated expressions. Additionally, in our post-hoc analysis we find that the Earth Movers Distance provides a useful metric for identifying similar expressions in the design space that lead to classification confusion.

Collaborative robots must effectively communicate their internal state to humans to enable a smooth interaction. Nonverbal communication is widely used to communicate information during human-robot interaction, however, such methods may also be misunderstood, leading to communication errors. In this work, we explore modulating the acoustic parameter values (pitch bend, beats per minute, beats per loop) of nonverbal auditory expressions to convey functional robot states (accomplished, progressing, stuck). We propose a reinforcement learning (RL) algorithm based on noisy human feedback to produce accurately interpreted nonverbal auditory expressions. The proposed approach was evaluated through a user study with 24 participants. The results demonstrate that: 1. Our proposed RL-based approach is able to learn suitable acoustic parameter values which improve the users' ability to correctly identify the state of the robot. 2. Algorithm initialization informed by previous user data can be used to significantly speed up the learning process. 3. The method used for algorithm initialization strongly influences whether participants converge to similar sounds for each robot state. 4. Modulation of pitch bend has the largest influence on user association between sounds and robotic states.

Collaborative robots must effectively communicate their internal state to humans to enable a smooth interaction. Nonverbal communication is widely used to communicate information during human-robot interaction, however, such methods may also be misunderstood, leading to communication errors. In this work, we explore modulating the acoustic parameter values (pitch bend, beats per minute, beats per loop) of nonverbal auditory expressions to convey functional robot states (accomplished, progressing, stuck). We propose a reinforcement learning (RL) algorithm based on noisy human feedback to produce accurately interpreted nonverbal auditory expressions. The proposed approach was evaluated through a user study with 24 participants. The results demonstrate that: i) Our proposed RL-based approach is able to learn suitable acoustic parameter values which improve the users' ability to correctly identify the state of the robot. ii) Algorithm initialization informed by previous user data can be used to significantly speed up the learning process. iii) The method used for algorithm initialization strongly influences whether participants converge to similar sounds for each robot state. iv) Modulation of pitch bend has the largest influence on user association between sounds and robotic states.