Recent advances in code generation and reasoning have enabled the growing use of large language models (LLMs) in robotics. The design of traditional robotic feedback controllers often requires extensive expert knowledge and iterative computational effort, motivating the exploration of LLM-assisted controller development. However, existing efforts are largely restricted to overly simplified systems, provide limited comparison with human-designed controllers, and lack validation on real-world robotic platforms. To address these gaps, this work investigates the use of LLMs in robotic feedback control by focusing on adaptive control and proposing an LLM-guided adaptive compensator. Instead of generating a complete controller from scratch, the LLM is guided to design a compensator that modulates the response of an unknown system to match that of a predefined reference system, thereby achieving adaptivity. The proposed approach is evaluated using five adaptive controllers on a two-degree-of-freedom (DoF) soft robot and a one-DoF shoulder joint of a humanoid robot. Both simulation and real-world experiments demonstrate that the LLM-guided adaptive compensator achieves performance comparable to four conventional adaptive controllers. Lyapunov-based analysis further establishes the stability and generalization capability of the proposed compensator, while analysis of the reasoning process suggests a more structured design approach. This study introduces a novel framework for integrating LLMs into robotic feedback control, with promising practical applicability.

Intubation is essential for ventilating critically ill patients and involves precise maneuvering of a laryngoscope to place an endotracheal tube (ETT). However, training for this procedure is fraught with challenges. Traditional methods, relying on manikins or training with a single sensing modality, fail to adequately convey important interaction information. Such challenge is heightened in pediatric intubation due to anatomical differences that demand greater precision. Furthermore, integrating multiple sensing modalities into a laryngoscope without changing its size presents a significant design challenge, critical for maintaining realistic training scenarios. To overcome these obstacles, we developed a sensorized laryngoscope system equipped with a force-torque sensor, a 9-axis inertial measurement unit (IMU), and tactile sensors. This system, validated in a preliminary user study, provides online feedback on angles, forces, and grip strength through an online feedback GUI. Adopting a learning-by-demonstration approach with both experts and novices, the initial validation confirmed the system’s potential, paving the way for expanded trials with more participants.

Given the importance of affective touch in human interactions, technology designers are increasingly attempting to bring this modality to the core of interactive technology. Advances in haptics and touch-sensing technology have been critical to fostering interest in this area. In this survey, we review how affective touch is investigated to enhance and support the human experience with or through technology. We explore this question across three different research areas to highlight their epistemology, main findings, and the challenges that persist. First, we review affective touch technology through the human–computer interaction literature to understand how it has been applied to the mediation of human–human interaction and its roles in other human interactions particularly with oneself, augmented objects/media, and affect-aware devices. We further highlight the datasets and methods that have been investigated for automatic detection and interpretation of affective touch in this area. In addition, we discuss the modalities of affective touch expressions in both humans and technology in these interactions. Second, we separately review how affective touch has been explored in human–robot and real-human–virtual-human interactions where the technical challenges encountered and the types of experience aimed at are different. We conclude with a discussion of the gaps and challenges that emerge from the review to steer research in directions that are critical for advancing affective touch technology and recognition systems. In our discussion, we also raise ethical issues that should be considered for responsible innovation in this growing area.

A patient would contract surface muscles as a reaction called muscle guarding when experiencing discomfort and pain during physical palpation. This reaction carries important information about an affected location. Training physicians to regulate palpation forces to elicit just enough muscle tension is a challenge using real patients. Tunable stiffness mechanisms enabled by soft robotics can be effectively integrated into medical simulator designs for effective clinical education. In this paper, we propose a controllable stiffness muscle layer to simulate guarding for abdominal palpation training. Designs with soft, fine, and rigid granular jamming, stretchable and non-stretchable layer jamming mechanisms were tested and evaluated as methods to create controllable stiffness muscle. User studies have been carried out on 10 naive participants to differentiate the tense and relaxed abdomen with the proposed jamming mechanisms. Muscle samples made of ground coffee (fine granular jamming) and latex layers (stretchable layer jamming) show good usability in simulating abdomen with different stiffness with at least 75% of the user data exhibits more than 70% of decision accuracy for both tested palpation gestures (single finger and multiple fingers) after short pre-training.

Robotic phantoms enable advanced physical examination training before using human patients. In this article, we present an abdominal phantom for palpation training with controllable stiffness liver nodules that can also sense palpation forces. The coupled sensing and actuation approach is achieved by pneumatic control of positive-granular jammed nodules for tunable stiffness. Soft sensing is done using the variation of internal pressure of the nodules under external forces. This article makes original contributions to extend the linear region of the neo-Hookean characteristic of the mechanical behavior of the nodules by 140% compared to no-jamming conditions and to propose a method using the organ level controllable nodules as sensors to estimate palpation position and force with a root-mean-square error of 4% and 6.5%, respectively. Compared to conventional soft sensors, the method allows the phantom to sense with no interference to the simulated physiological conditions when providing quantified feedback to trainees, and to enable training following current bare-hand examination protocols without the need to wear data gloves to collect data.