This study investigates whether adding ground compliance to visual feedback (VF) gait training is more effective at increasing push-off force (POF) compared to using VF alone, with implications for gait rehabilitation. Ten healthy participants walked on a custom split-belt treadmill. All participants received real-time visual feedback of their ground reaction forces. One group also experienced changes in ground compliance, while a control group received only visual feedback. Intentional increases in propulsive ground reaction forces (POF) were successfully achieved and sustained post-intervention, especially in the group that experienced ground compliance. This group also demonstrated lasting after-effects in muscle activity and joint kinematics, indicating a more robust learning of natural strategies to increase propulsion. This work demonstrates how visual and proprioceptive systems coordinate during gait adaptation. It uniquely shows that combining ground compliance with visual feedback enhances the learning of propulsive forces, supporting the potential use of compliant terrain in long-term rehabilitation targeting propulsion deficits, such as those following a stroke.

Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind. That is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which targets the realistic yet underexplored setting where users refine their search for a mentally envisioned image through multi-round interactions with an image search engine. Central to successful interactive retrieval is the capability of machines to provide users with clear, actionable feedback; however, existing methods rely on indirect or abstract verbal feedback, which can be ambiguous, misleading, or ineffective for users to refine the query. To overcome this, we propose GenIR, a generative multi-round retrieval paradigm leveraging diffusion-based image generation to explicitly reify the AI system's understanding at each round. These synthetic visual representations provide clear, interpretable feedback, enabling users to refine their queries intuitively and effectively. We further introduce a fully automated pipeline to generate a high-quality multi-round MIR dataset. Experimental results demonstrate that GenIR significantly outperforms existing interactive methods in the MIR scenario. This work establishes a new task with a dataset and an effective generative retrieval method, providing a foundation for future research in this direction

Abstract-- Large Language Models are increasingly used in robotics for task planning, but their reliance on textual inputs limits their adaptability to real-world changes and failures. T o address these challenges, we propose LERa -- L ook, E xplain, R epla n -- a Visual Language Model-based replanning approach that utilizes visual feedback. Unlike existing methods, LERa requires only a raw RGB image, a natural language instruction, an initial task plan, and failure detection -- without additional information such as object detection or predefined conditions that may be unavailable in a given scenario. The replanning process consists of three steps: (i) Look -- where LERa generates a scene description and identifies errors; (ii) Explain -- where it provides corrective guidance; and (iii) Replan -- where it modifies the plan accordingly. LERa is adaptable to various agent architectures and can handle errors from both dynamic scene changes and task execution failures. We evaluate LERa on the newly introduced ALFRED-ChaOS and VirtualHome-ChaOS datasets, achieving a 40% improvement over baselines in dynamic environments. In tabletop manipulation tasks with a predefined probability of task failure within the PyBullet simulator, LERa improves success rates by up to 67%. Further experiments, including real-world trials with a tabletop manipulator robot, confirm LERa's effectiveness in replanning. We demonstrate that LERa is a robust and adaptable solution for error-aware task execution in robotics. The project page is available at https://lera-robo.github.io. I. INTRODUCTION Large Language Models (LLMs) trained on Internet-scale data can solve problems that they were not originally designed for [1].

Designing effective reward functions remains a major challenge in reinforcement learning (RL), often requiring considerable human expertise and iterative refinement. Recent advances leverage Large Language Models (LLMs) for automated reward design, but these approaches are limited by hallucinations, reliance on human feedback, and challenges with handling complex, multi-step tasks. In this work, we introduce Reward Evolution with Graph-of-Thoughts (RE-GoT), a novel bi-level framework that enhances LLMs with structured graph-based reasoning and integrates Visual Language Models (VLMs) for automated rollout evaluation. RE-GoT first decomposes tasks into text-attributed graphs, enabling comprehensive analysis and reward function generation, and then iteratively refines rewards using visual feedback from VLMs without human intervention. Extensive experiments on 10 RoboGen and 4 ManiSkill2 tasks demonstrate that RE-GoT consistently outperforms existing LLM-based baselines. On RoboGen, our method improves average task success rates by 32.25%, with notable gains on complex multi-step tasks. On ManiSkill2, RE-GoT achieves an average success rate of 93.73% across four diverse manipulation tasks, significantly surpassing prior LLM-based approaches and even exceeding expert-designed rewards. Our results indicate that combining LLMs and VLMs with graph-of-thoughts reasoning provides a scalable and effective solution for autonomous reward evolution in RL.

--Real-time human-device interaction in industrial Metaverse faces challenges such as high computational load, limited bandwidth, and strict latency. This paper proposes a task-oriented edge-assisted cross-system framework using digital twins (DTs) to enable responsive interactions. By predicting operator motions, the system supports: 1) proactive Metaverse rendering for visual feedback, and 2) preemptive control of remote devices. The DTs are decoupled into two virtual functions--visual display and robotic control--optimizing both performance and adaptability. T o enhance generalizability, we introduce the Human-In-The-Loop Model-Agnostic Meta-Learning (HITL-MAML) algorithm, which dynamically adjusts prediction horizons. Evaluation on two tasks demonstrates the framework's effectiveness: in a Trajectory-Based Drawing Control task, it reduces weighted RMSE from 0.0712 m to 0.0101 m; in a real-time 3D scene representation task for nuclear decommissioning, it achieves a PSNR of 22.11, SSIM of 0.8729, and LPIPS of 0.1298. These results show the framework's capability to ensure spatial precision and visual fidelity in real-time, high-risk industrial environments. Industrial Metaverse represents an integrated virtual ecosystem that extends the concept of the Metaverse to specific industrial sectors, merging physical and digital realms. It explores the transformative potential of teleoperation, real-time collaboration, and synchronization within high-risk industries, driving substantial advancements in industrial operations [1]. Digital twins (DTs) are a key enabler within the larger framework of industrial Metaverse, facilitating real-time data interaction and providing highly accurate virtual models of physical assets [2].

This paper introduces the Ambient Intelligence Rehabilitation Support (AIRS) framework, an advanced artificial intelligence-based solution tailored for home rehabilitation environments. AIRS integrates cutting-edge technologies, including Real-Time 3D Reconstruction (RT-3DR), intelligent navigation, and large Vision-Language Models (VLMs), to create a comprehensive system for machine-guided physical rehabilitation. The general AIRS framework is demonstrated in rehabilitation scenarios following total knee replacement (TKR), utilizing a database of 263 video recordings for evaluation. A smartphone is employed within AIRS to perform RT-3DR of living spaces and has a body-matched avatar to provide visual feedback about the excercise. This avatar is necessary in (a) optimizing exercise configurations, including camera placement, patient positioning, and initial poses, and (b) addressing privacy concerns and promoting compliance with the AI Act. The system guides users through the recording process to ensure the collection of properly recorded videos. AIRS employs two feedback mechanisms: (i) visual 3D feedback, enabling direct comparisons between prerecorded clinical exercises and patient home recordings and (ii) VLM-generated feedback, providing detailed explanations and corrections for exercise errors. The framework also supports people with visual and hearing impairments. It also features a modular design that can be adapted to broader rehabilitation contexts. AIRS software components are available for further use and customization.

Enabling humanoid robots to reliably execute complex multi-step manipulation tasks is crucial for their effective deployment in industrial and household environments. This paper presents a hierarchical planning and control framework designed to achieve reliable multi-step humanoid manipulation. The proposed system comprises three layers: (1) a low-level RL-based controller responsible for tracking whole-body motion targets; (2) a mid-level set of skill policies trained via imitation learning that produce motion targets for different steps of a task; and (3) a high-level vision-language planning module that determines which skills should be executed and also monitors their completion in real-time using pretrained vision-language models (VLMs). Experimental validation is performed on a Unitree G1 humanoid robot executing a non-prehensile pick-and-place task. Over 40 real-world trials, the hierarchical system achieved a 73% success rate in completing the full manipulation sequence. These experiments confirm the feasibility of the proposed hierarchical system, highlighting the benefits of VLM-based skill planning and monitoring for multi-step manipulation scenarios. See https://vlp-humanoid.github.io/ for video demonstrations of the policy rollout.

Abstract--State-of-the-art upper limb myoelectric prostheses often use pattern recognition (PR) control systems that translate electromyography (EMG) signals into desired movements. As prosthesis movement complexity increases, users often struggle to produce sufficiently distinct EMG patterns for reliable classification. Existing training typically involves heuristic, trial-and-error user adjustments to static decoder boundaries. Goal: We introduce the Reviewer, a 3D visual interface projecting EMG signals directly into the decoder's classification space, providing intuitive, real-time insight into PR algorithm behavior. This structured feedback reduces cognitive load and fosters mutual, data-driven adaptation between user-generated EMG patterns and decoder boundaries. Methods: A 10-session study with 12 able-bodied participants compared PR performance after motor-based training and updating using the Reviewer versus conventional virtual arm visualization. Performance was assessed using a Fitts law task that involved the aperture of the cursor and the control of orientation. Results: Participants trained with the Reviewer achieved higher completion rates, reduced overshoot, and improved path efficiency and throughput compared to the standard visualization group. Significance: The Reviewer The Reviewer introduces decoder-informed motor training, facilitating immediate and consistent PR-based myoelectric control improvements. By iteratively refining control through real-time feedback, this approach reduces reliance on trial-and-error recalibration, enabling a more adaptive, self-correcting training framework. Conclusion: The 3D visual feedback significantly improves PR control in novice operators through structured training, enabling feedback-driven adaptation and reducing reliance on extensive heuristic adjustments.

Individuals with upper limb mobility impairments often require assistive technologies to perform activities of daily living. While gaze-tracking has emerged as a promising method for robotic assistance, existing solutions lack sufficient feedback mechanisms, leading to uncertainty in user intent recognition and reduced adaptability. This paper presents the MIHRAGe interface, an integrated system that combines gaze-tracking, robotic assistance, and a mixed-reality to create an immersive environment for controlling the robot using only eye movements. The system was evaluated through an experimental protocol involving four participants, assessing gaze accuracy, robotic positioning precision, and the overall success of a pick and place task. Results showed an average gaze fixation error of 1.46 cm, with individual variations ranging from 1.28 cm to 2.14 cm. The robotic arm demonstrated an average positioning error of +-1.53 cm, with discrepancies attributed to interface resolution and calibration constraints. In a pick and place task, the system achieved a success rate of 80%, highlighting its potential for improving accessibility in human-robot interaction with visual feedback to the user.

-- Recent advancements in robotic loco-manipulation have leveraged Virtual Reality (VR) to enhance the precision and immersiveness of teleoperation systems, significantly outperforming traditional methods reliant on 2D camera feeds and joystick controls. Despite these advancements, challenges remain, particularly concerning user experience across different setups. This paper introduces a novel VR-based teleoperation framework designed for a robotic manipulator integrated onto a mobile platform. Central to our approach is the application of Gaussian splattering, a technique that abstracts the manipulable scene into a VR environment, thereby enabling more intuitive and immersive interactions. Users can navigate and manipulate within the virtual scene as if interacting with a real robot, enhancing both the engagement and efficacy of teleoperation tasks. Two-thirds (66%) of participants completed tasks faster, achieving an average time reduction of 43%. Additionally, 93% preferred the Gaussian Splat interface overall, with unanimous (100%) recommendations for future use, highlighting improvements in precision, responsiveness, and sit-uational awareness. Finally, we demonstrate the effectiveness of our framework through real-world experiments in two distinct application scenarios, showcasing the practical capabilities and versatility of the Splat-based VR interface.