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Reward function, as an incentive representation that recognizes humans' agency and rationalizes humans' actions, is particularly appealing for modeling human behavior in human-robot interaction. Inverse Reinforcement Learning is an…
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven…
Human interaction experience plays a crucial role in the effectiveness of human-machine collaboration, especially as interactions in future systems progress towards tighter physical and functional integration. While automation design has…
Shared autonomy teleoperation can guarantee safety, but does so by reducing the human operator's control authority, which can lead to reduced levels of human-robot agreement and user satisfaction. This paper presents a novel haptic shared…
As robotic technology advances, the barriers to the coexistence of humans and robots are slowly coming down. Application domains like elderly care, collaborative manufacturing, collaborative manipulation, etc., are considered the need of…
Recent advancements in Large Language Models (LLMs) have demonstrated substantial capabilities in enhancing communication and coordination in multi-robot systems. However, existing methods often struggle to achieve efficient collaboration…
The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role…
As the use of Augmented Reality (AR) to enhance interactions between human agents and robotic systems in a work environment continues to grow, robots must communicate their intents in informative yet straightforward ways. This improves the…
Humans make decisions and act alongside other humans to pursue both short-term and long-term goals. As a result of ongoing progress in areas such as computing science and automation, humans now also interact with non-human agents of varying…
Human-robot co-carrying tasks reveal their potential in both industrial and everyday applications by leveraging the strengths of both parties. Effective control of robots in these tasks requires managing the energy level in the closed-loop…
In the last two decades the scientific community has shown a great interest in understanding and shaping the interaction mechanisms between humans and robots. The interaction implies communication between two dyadic agents and, if the type…
Robots are moving beyond industrial settings into creative, educational, and public environments where interaction is open-ended and improvisational. Yet much of human-AI-robot interaction remains framed around performance and efficiency,…
Communication between humans and artificial agents is essential for their interaction. This is often inspired by human communication, which uses gestures, facial expressions, gaze direction, and other explicit and implicit means. This work…
Multi-agent human-robot co-manipulation is a poorly understood process with many inputs that potentially affect agent behavior. This paper explores one such input known as interaction force. Interaction force is potentially a primary…
Humans can leverage physical interaction to teach robot arms. This physical interaction takes multiple forms depending on the task, the user, and what the robot has learned so far. State-of-the-art approaches focus on learning from a single…
Human-to-human conversation is not just talking and listening. It is an incremental process where participants continually establish a common understanding to rule out misunderstandings. Current language understanding methods for…
Teleoperated robotic characters can perform expressive interactions with humans, relying on the operators' experience and social intuition. In this work, we propose to create autonomous interactive robots, by training a model to imitate…
High-precision control tasks present substantial challenges for reinforcement learning (RL) algorithms, frequently resulting in suboptimal performance attributed to network approximation inaccuracies and inadequate sample quality.These…
Conventional reinforcement learning (RL) ap proaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this limitation, rein…
In this paper, we extended the method proposed in [21] to enable humans to interact naturally with autonomous agents through vocal and textual conversations. Our extended method exploits the inherent capabilities of pre-trained large…