Gesture
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Imitation of Arm Gestures by the Semi-Humanoid Robot NICO
2026-07-20 17:37:57Anastasiya Ihnatovich, Igor Farka\v{s}Abstract
Seamless human-robot interaction (HRI) requires a number of perceptual and motor abilities from the robot, one of them being the imitation of human gestures. Humanoid robots have an advantage in HRI thanks to their anthropomorphic features. In this work, we develop a system for imitation of human arm gestures by the semi-humanoid robot NICO based on analytical geometry and a pretrained MediaPipe pose-estimation model. For each input RGB frame, 3D coordinates of relevant human body landmarks, including arm joints and hand keypoints, are obtained using the MediaPipe framework. Joint angles are then computed from these coordinates using derived geometric relations. Finally, the computed angles are properly mapped to NICO's motor configuration and executed in a predefined motion sequence. Preliminary experiments on several representative arm gestures with six participants of different height indicate that the proposed method can produce meaningful imitative motions from monocular RGB input only, while also highlighting limitations in more complex poses and wrist-related movements.
Abstract (translated)
URL
https://arxiv.org/abs/2607.18197
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From Perception to Assistance: Open-Vocabulary Shared Autonomy for Robotic Manipulation
2026-07-19 16:15:14Murilo Vinicius da Silva, Ricardo V. Godoy, Juliano Negri, Gustavo J. G. Lahr, Ranulfo Bezerra, Marcelo BeckerAbstract
Teleoperating a robotic manipulator in industrial environments demands precision that camera-based interfaces alone struggle to deliver. The operator must align the end-effector with a target in clutter, under limited depth perception, and without colliding with the surrounding structures. This paper presents a shared-autonomy framework that assists the operator throughout this process. A single RGB-D camera captures the operator's arm motion and hand gestures without wearables, fiducials, or a calibration stage. The intended target is specified by a free-form text prompt, grounded by a vision-language model in the robot's gripper camera, and tracked across its onboard cameras by a promptable video-segmentation model, resulting in a grasp frame continuously separated from the obstacle map. Every commanded motion is executed by a GPU-accelerated model-predictive controller that enforces self- and environment-collision avoidance against an online volumetric reconstruction, while a potential field corrects the operator's reference toward the grounded target during the final approach. An autonomous mode can be gesture-triggered to complete the grasp on the same target without a separate perception pipeline. The framework is validated on a quadruped mobile manipulator. The interface achieves a positional RMSE of 59 mm relative to motion-capture ground truth, and the controller keeps the arm at least 18 cm from obstacles while the operator deliberately commands the arm into them by 6 cm. In an industrial valve manipulation and a pick-and-place task, the full framework succeeded in all trials, while ablating either the collision or the assistance module produced failures through complementary mechanisms, and autonomous execution succeeded in four of five trials per task.
Abstract (translated)
URL
https://arxiv.org/abs/2607.17323
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Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
2026-07-17 00:09:47Yuxuan Xie, Nicolas Pugeault, Chongfeng Wei, Hubert P. H. Shum, Edmond S. L. HoAbstract
Pedestrian trajectory prediction from an on-board ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian. The task becomes even more challenging since pedestrian intention is often ambiguous from historical observations alone, leading to an inherently multimodal distribution over future trajectories. Existing CVAE-based predictors learn a latent representation by aligning the prior conditioned on trajectory history with the posterior conditioned on both history and future trajectories. This alignment may suppress multimodality in the latent space, leading to mixed-mode predictions that interpolate between distinct future behaviours. In this paper, we propose MMPM, a mode-aware framework that separately models future trajectory distributions into semantically meaningful modes based on the pedestrian's crossing behavior. MMPM consists of two modules: behavior-aware Pedestrian Interaction Module (PIM) that jointly captures pedestrian-vehicle and pedestrian-environment interactions by introducing gaze and hand gestures, and a CVAE-based Mode-aware Trajectory Predictor (MTP) module to model the future trajectory distributions in two modes, crossing and non-crossing the road, separately. A query-based decoder further enforces mode consistency during decoding. Experiments on PIE and JAAD datasets show that our method surpasses state-of-the-art baselines. Our proposed MTP is model-agnostic, which can be integrated into existing frameworks such as BiTrap and SGNet to further improve future trajectory prediction performance. We additionally introduce a data-driven validation protocol that retrieves spatio-temporally consistent real-world trajectories from the same scene and evaluates predictions against these retrieved examples, demonstrating improved frame-wise displacement errors over previous work.
Abstract (translated)
URL
https://arxiv.org/abs/2606.18824
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VTAP Gripper: Synergizing Fingertip Sensing and a Visuo-Tactile Active Palm for Dexterous In-Hand Manipulation
2026-07-16 20:41:00Yuhao Zhou, Sheeraz Athar, Zhixian Hu, Binghao Huang, Yunzhu Li, Juan Wachs, Yu SheAbstract
This paper presents a tactile-reactive gripper that integrates a Visuo-Tactile Active Palm (VTAP) and compliant, reconfigurable fingers equipped with tactile array sensors. The design exploits structured finger-palm synergy and multi-modal perception to achieve both robust grasping and fine manipulation. The actuated bi-modal palm seamlessly combines long-range visual localization with contact-rich tactile feedback, substantially extending the system's manipulation capability. To bridge the embodiment gap between human hand motion and the heterogeneous three-finger structure, we further propose a staged, gesture-conditioned retargeting framework for dexterous teleoperation. Extensive experiments validate the system across a range of challenging tasks: reactive grasping of YCB and fragile objects, in-hand syringe reorientation and plunger actuation, singulation of clustered objects down to 3 mm in diameter, and vision-tactile peg-in-hole insertion. Results demonstrate that high manipulation performance can be achieved through coordinated finger-palm interaction and multi-modal sensing, without resorting to high degrees of freedom anthropomorphic designs. The VTAP gripper and its retargeting framework offer a practical reference architecture for dexterous gripper design, manipulation, and contact-rich data collection in support of learning-based approaches. Project webpage: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2607.15448
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S.E.A.G.R: A Socially and Emotionally Aware Greeting Robot Framework with Dual-Layer Cultural and Affective Modulation
2026-07-16 17:15:59Sajjad Hussain, Ranveer Bhura, Amandip Dutta, Shwetangshu BiswasAbstract
This paper presents SEAGR (Socially and Emotionally Aware Greeting Robot), a robotic greeting framework designed for human-robot interaction environments involving users from diverse cultural backgrounds and different emotional states. Since greeting behaviour strongly influences first impressions, user comfort, and trust, robots operating in public spaces must be able to interact in a socially appropriate and adaptive manner. However, many existing systems still rely on static greeting routines that do not account for cultural variation, emotional context, or interpersonal distance. SEAGR introduces a dual-layer modulation framework in which cultural identity determines the appropriate greeting type, while affective cues influence how that greeting is executed. The system combines context-aware cultural mapping, emotion-based gesture modulation, and proxemic regulation within a unified Sense-Think-Act architecture. A low-cost prototype is implemented using a USB camera, ultrasonic sensor, Arduino-controlled servos, and a laptop-based Python processing system. This work is presented as a system design and proof-of-concept; empirical validation through user studies is explicitly acknowledged as a current limitation and is identified as the primary direction for future work.
Abstract (translated)
URL
https://arxiv.org/abs/2607.16341
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An Intelligent-Cloud Edge Multimodal Interaction System for Robots
2026-07-16 07:39:18Zihan Guo, Xiaoqi LiAbstract
Robust human-robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable task planning under limited onboard computing resources. This paper presents a cloud-edge multimodal interaction framework that integrates an enhanced YOLO-based gesture detector with coordinated large language model (LLM) and vision-language model (VLM) agents. The proposed detector, incorporates the Convolutional Block Attention Module (CBAM) into the neck and replaces the baseline bounding-box regression objective with Distance-IoU (DIoU) loss. These modifications improve feature discrimination and localization for small or partially occluded gestures in complex backgrounds. The cloud layer performs gesture detection, scene understanding, multimodal fusion, and action planning, whereas the TonyPi robot locally handles data acquisition, communication, action execution, and feedback. Experiments on a public gesture dataset and a custom dataset show that YOLO-DC achieves precision values of 98.9% and 95.0%, with mAP@0.5 values of 90.7% and 92.7%, respectively. System-level evaluation yields success rates of 95%, 88%, and 82% for single-action, composite-action, and vision-dependent tasks. A 30 participant evaluation yields an overall mean satisfaction score of 3.69 out of 5. These results demonstrate the feasibility of combining refined gesture detection with multimodal agents for resource-constrained robotic interaction.
Abstract (translated)
URL
https://arxiv.org/abs/2607.14675
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GMoT: Gated Motion-Aware Tokenization for Fine-Grained Micro-Gesture Video Reasoning with Multimodal LLMs
2026-07-15 09:37:58Taorui Wang, Wei Xia, Hui Ma, Zijia Song, Jiayu Zhang, Zeheng Wang, Yong Xu, Zitong YuAbstract
Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise. While Multimodal Large Language Models (MLLMs) excel at general video understanding, they inherently struggle with subtle kinematics and often rely on static posture priors. To this end, we propose GMoT, a Gated Motion-Aware Tokenization module that explicitly distills sparse kinematic evidence into a compact sequence prior to temporal modeling. GMoT dynamically spotlights action-relevant regions via spatially weighted pooling, extracts adjacent-frame temporal differencing to capture precise motion energy, and adaptively fuses these cues into the visual stream using a conservatively initialized semantic gate. To transition from simple classification to evidence-grounded reasoning, we further introduce a progressive reward-guided policy refinement paradigm, supported by a semi-supervised annotation pipeline that generates anatomically focused captions. Beyond achieving the best Top-1 accuracy among the compared methods on iMiGUE (67.32\%) and SMG (73.11\%), improving the Qwen3-VL-8B baseline by +6.80 and +3.11 points, our framework introduces Body-Region Grounding (BRG) Recall as an anatomical-grounding proxy conditioned on correct predictions, together with an overlapping-label cross-domain transfer protocol between iMiGUE and SMG. Extensive evaluations demonstrate that our GMoT-augmented model improves in-domain accuracy, retains clear gains under label-preserving corruptions, and improves accuracy-oriented cross-domain transfer under explicit small-split caveats while maintaining high anatomical grounding in its generated rationales.
Abstract (translated)
URL
https://arxiv.org/abs/2607.16322
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Real-Time sEMG-Based Telecontrol of an Assistive Robotic Arm Using a 1D Convolutional Neural Network
2026-07-14 15:34:28Edgar Manacorda, Mena Samir Kama Abouseffien, Olivier Lecompte, Amandine Gesta, Abolfazl MohebbiAbstract
Motor impairments affecting the upper limb significantly reduce autonomy in daily activities, particularly for tasks involving object manipulation. Assistive robotic arms offer a promising solution, provided they can be controlled in an intuitive, reliable, and responsive manner. Among human--machine interface approaches, surface electromyography (sEMG) enables non-invasive access to muscle activity and thus to the user's motor intentions. This work proposes a real-time sEMG-based interface for the teleoperation of an assistive robotic arm. The system relies on four-channel sEMG acquisition, signal preprocessing, segmentation into sliding windows, and classification using a one-dimensional convolutional neural network (CNN). Several real-time strategies are investigated, including threshold-based onset detection, a two-stage classification approach (rest vs movement followed by gesture recognition), and a single classifier handling both rest and five gestures. The complete pipeline is implemented and evaluated both in simulation and on a real robotic platform. The CNN-based approach achieves high classification performance, with a test accuracy above 90\% and strong generalization on experimentally acquired signals. The system exhibits stable real-time behavior, with an average latency of approximately 0.32 s consistent with the chosen windowing strategy, and the robot can be controlled reliably using discrete gestures, producing coherent and smooth movements in both simulated and real environments. These findings demonstrate the feasibility of sEMG-based telecontrol for assistive robotics and highlight the importance of integrating signal processing, deep learning, and control strategies within a unified real-time framework. Future work may explore hybrid control approaches combining sEMG with additional sensing modalities to further improve robustness and usability.
Abstract (translated)
URL
https://arxiv.org/abs/2607.16310
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Requirement-Driven Design of Whole-Body Social Tactile Sensing via Virtual Human-Robot Interaction
2026-07-13 15:23:45Dakarai Crowder, Ruohan Zhang, Alexis E. Block, Wenzhen YuanAbstract
Tactile sensing for social-physical human-robot interaction (spHRI) is designed in a hardware-driven manner, where predefined sensor configurations constrain coverage, spatial resolution, and the range of recognizable gestures. We propose a requirement-driven framework that derives sensing requirements, specifically spatial resolution and placement, directly from interaction data. Using a VR-based platform with haptic feedback, we collected high-resolution whole-body contact distributions across multiple social scenarios, from which we identified nine recurring social touch gestures. Eight gestures were selected for controlled data collection with 18 participants, yielding an open-source dataset of 5,520 trials. Analysis of contact distributions and simulated tactile encodings provides quantitative baselines for skin coverage and sensor density on a humanoid robot platform. While demonstrated on a single robot platform, the methodology is designed to be transferable to other robot morphologies, potentially enabling morphology-specific sensing requirements to be derived prior to hardware fabrication.
Abstract (translated)
URL
https://arxiv.org/abs/2607.11690
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A Model for Mediating Multi-Modal Human Intent into Safe Maneuvers for UAVs
2026-07-13 15:04:57Sofia Nelson, Dalal Alrajeh, Pedro Antonio Alarcon Granadeno, Jane Cleland-HuangAbstract
Direct human interaction with autonomous UAV systems can be enabled through modalities such as speech, gestures, and graphical interfaces. However, interpreting such inputs as directly executable commands introduces safety risks in dynamic environments. Operator requests may conflict with terrain constraints, inter-UAV separation requirements, or flight-envelope limitations. In this paper, we present a requirements-governed maneuver-response model that mediates multi-modal human intent into safe UAV maneuvers by treating operator inputs as bounded maneuver requests rather than direct commands. Requested maneuvers are mapped to constrained motion primitives and processed through a structured request-evaluate-execute pipeline. Each request is interpreted with associated confidence, validated against terrain, separation, workspace, and flight-envelope constraints, and either constrained, rejected, or executed under continuous runtime monitoring. We further formalize the approach as a requirements-based specification model in which maneuver primitives are associated with explicit preconditions, invariants, guard conditions, and postconditions governing admissibility, execution safety, and emergency handling. These requirements support runtime verification and future reactive synthesis approaches. We present an initial lab-based validation demonstrating that voice and GUI-based inputs can be reliably interpreted and safely executed as constrained maneuver requests.
Abstract (translated)
URL
https://arxiv.org/abs/2607.11654
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fog: Expressing Motion and Emotion through Function Composition of AI-Generated Code
Abstract
Motion and emotion are core parts of intelligent, expressive behavior. In this paper, we introduce fog, a function composition framework for implementing and compose motion functions. We demonstrate how fog can be used to express motion and emotion in Heider-Simmel style animations. This code generation framework can help users generate functions for verbs, adverbs, gestures, and emotions to create an open-ended motion vocabulary. It is complemented by an animation editor that helps users refine motion through direct manipulation and dynamically generated UI. We evaluate our approach with a perceptual evaluation, where we test 452 fog-generated animations to see if people can recognize the semantic meaning of the motion. We find that fog's motion functions can be recognized at 68% accuracy, a 2.68x improvement over a chance baseline. In a mixed-methods user study with professionals and novices, we show that fog in interface form can support users with more rapid iteration, exploration, and control.
Abstract (translated)
URL
https://arxiv.org/abs/2607.07952
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A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals
2026-07-08 18:29:52Pragatheeswaran Vipulanandan, Kamal Premaratne, Manohar MurthiAbstract
For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential. Surface electromyography (sEMG) signals obtained from the forearm are commonly employed for this purpose. In this paper, we present a novel approach for sEMG representation that utilizes graph networks which contain information about muscle activation patterns in the forearm. Based on these graph networks, we have developed a machine learning algorithm capable of real-time hand gesture recognition using a graph neural network. The algorithm's performance was evaluated using sEMG signals acquired from myoband, which has 8 electrodes placed around the forearm, involving 8 healthy subjects. The proposed method demonstrated an average classification accuracy of 99\%, surpassing the performance of state-of-the-art techniques. The average time for both graph construction and prediction stood at 48ms utilizing a M1 pro CPU, rendering the approach well-suited for real-time applications.
Abstract (translated)
URL
https://arxiv.org/abs/2607.07850
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Communicative Efficiency of Single vs. Multi-Axis Robot Neck Motion
2026-07-08 13:23:56Chapa Sirithunge, Haewon Jeong, Qinghua Guan, Fumiya Iida, Josie HughesAbstract
Nonverbal communication through head and neck movement is fundamental to human social signalling, yet how robotic neck morphology translates motion into communicative information remains poorly understood. We present an information-theoretic framework characterising robot neck movement as a communication channel, quantifying information transmitted and energy expended across varied configurations. Using a robotic neck platform, we recorded 84 video stimuli spanning three rotational degrees of freedom (DoF), varying amplitude, acceleration, and frequency, measuring Shannon entropy of pixel-change signals alongside energy consumption. A perceptual study validated communicative interpretations of each motion. While humans typically engage one axis per gesture, robots are unconstrained by biological architecture, motivating tests up to 3 DoF. Yet communicative information peaks at two DoF and decreases at three despite rising energy cost, a phenomenon we term the morphological information bottleneck. Motion parameter effects were parameter-dependent, some additive, others non-linear. We introduce the Motor Information Space, a framework mapping entropy against energy to expose communicative efficiency across morphologies, in which the optimal configuration achieves 5.26 bits at competitive energy cost. Perception data further confirm multi-axis movements reduce clarity. These findings challenge the assumption that anatomical completeness improves robotic expressiveness, establishing a quantitative basis for morphological design in robots, especially humanoids.
Abstract (translated)
URL
https://arxiv.org/abs/2607.07390
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On the feasibility of dependency parsing of non-human sequences without a gold standard. Is evaluation possible in other species?
2026-07-07 17:47:26Ramon Ferrer-i-Cancho, Catherine Hobaiter, Thore Bergman, Morgan GustisonAbstract
Dependency parsing consists of finding a tree representation for a sequence. Unsupervised dependency parsing aims to develop parsing methods without a gold standard during model training. In human languages, an unsupervised parser can be evaluated because some gold standard is usually available or can be created. For other species, a gold standard is unknown. Thus one may conclude that it is impossible to determine the accuracy of an unsupervised parser and, consequently, dependency parsing is unfeasible in other species. However, here we apply recent advances in network science to demonstrate that the proportion of correct edges retrieved by a parser must be high for the sequences of vocalizations or gestures that non-human primates produce due to the fast decay of the sequence length distribution. In contrast, human language sequences lack that property. Therefore, evaluation without a gold standard is feasible in non-human primates but a hard problem in humans.
Abstract (translated)
URL
https://arxiv.org/abs/2607.06542
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Towards Real-World Applications with an Autonomous Powered Wheelchair
2026-07-07 15:21:33Simone Arreghini, Alessandro Giusti, Alex Bordini, Enrico Ferrara, Giovanni Fulgoni, Antonio PaolilloAbstract
Wheelchair users call for assistive mobility systems that provide active support, adapt to dynamic environments, and are intuitive and user-friendly. However, powered wheelchairs typically still provide limited autonomy and lack effective integration with advanced perception and navigation capabilities, particularly in complex real-world environments. This paper presents a preliminary study toward autonomous powered wheelchairs for real-world assistive mobility. We introduce a proof-of-concept prototype that integrates autonomous perception, gesture-based interaction, and navigation on a commercially available self-balancing powered wheelchair. The proposed system builds upon Genny Zero, a commercial self-balancing wheelchair that enables hands-free and intuitive operation through body-weight shifting. To extend its capabilities toward autonomous operation, we integrate an RGB-D camera for human-aware perception and interaction, together with a LiDAR sensor for localization and navigation. We demonstrate the integrated system in two assistive applications: (i) hailing, allowing users to call the wheelchair from a distance; and (ii) people-following, where the wheelchair follows a person using leader-follower strategies, including a constrained indoor navigation example. The results highlight the potential of combining autonomous robotics with assistive mobility platforms, while also showing the feasibility of the proposed integration and identifying the main technical challenges that must be addressed before moving toward user-ready, accessible, and intelligent mobility solutions. A video demonstrating the experimental setup and results is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2607.06383
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BitFair: A 12nm Bit-Serial CNN Accelerator with Learnable Early Termination and Adaptive Bit Ordering for Ultra-Low-Power XR Vision
Abstract
Extended Reality (XR) wearables require always-on perception within tight power envelopes of a few watts and motion-to-photon latency budgets below 20 ms, leaving only a few milliseconds for neural-network inference. Bit-serial computing is attractive for such energy-efficient neural network acceleration, but many existing architectures still process all bits even when ReLU sets the final output to zero. This paper presents BitFair, a software-hardware co-designed bit-serial CNN accelerator with learnable bit-level early termination and adaptive bit ordering, working under the ultra-low-power and strict latency requirements of XR applications. BitFair exploits dynamic bit-level sparsity by learning per-layer thresholds that trigger early termination when partial sums reliably predict that the final ReLU output will be zero. Furthermore, it searches for layer-wise bit orders that prioritize informative bits, maximizing early termination without sacrificing accuracy. A GlobalFoundries 12nm FinFET implementation with a core area of 0.34 mm^2, 104 KB on-chip memory, and voltage scaling from 0.55 to 0.70 V achieves sub-millisecond latency, up to 117.0 BTOPS/W, and 0.07 pJ/SOP. On IBM DVS128 Gesture and N-MNIST, BitFair achieves 96.5% and 97.7% accuracy, respectively, while improving effective energy efficiency by 4.0-22.1x and accuracy by up to 9.2% over prior fabricated XR vision accelerators.
Abstract (translated)
URL
https://arxiv.org/abs/2607.05445
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Sign in the Air to Unlock: An Interface for authentication in Virtual and Augmented Reality Powered by Point-Voxel Cross-Attention Network
2026-07-01 19:56:55Neda Abdolrahimi, Thiru Siddharth, Frank Sicongchen, Vir V PhohaAbstract
Significant advancement of immersive technologies such as Virtual and Augmented Reality (VR/AR) and their integration into diverse aspects of modern life need authentication interfaces that are secure, intuitive, and compatible with embodied interaction. Traditional methods such as passwords, PINs, and device-based logins, break immersion and rely on external hardware. Recent 3D-specific behavioral approaches, such as hand-gesture, eye-tracking, and electroencephalography (EEG)-based methods, offer promising alternatives but often require specialized sensors or constrain natural movement, limiting usability in dynamic environments. We present Sign in the Air to Unlock, an in-air signature interface that enables users to authenticate by signing naturally in 3D space which is a familiar, personal, and reproducible gesture. To realize this interface, we design a point-voxel Cross-Attention Network (PV-Net) that jointly models local motion dynamics and global spatial structure from 3D trajectories. The model is evaluated on two datasets: the public DeepAirSig dataset (1,800 signatures from 40 users) and ImmAirsig, a new dataset collected using Meta Quest 2 in immersive VR (880 samples from 22 users). PV-Net achieves an Equal Error Rate of 2.5% on DeepAirSig and 76% classification accuracy on ImmAirSig. These findings highlight the potential of 3D behavioral interfaces for seamless, user-centric authentication that merges security with natural interaction in immersive environments.
Abstract (translated)
URL
https://arxiv.org/abs/2607.01435
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HABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation
2026-06-30 13:58:19Jaehwi Song, Suchae Jeong, Byeongguk Jeon, Sungdong Kim, Minjoon Seo, Hyungmok Son, Kimin LeeAbstract
Large-scale demonstration datasets have been central to recent progress in general-purpose robot policies. However, existing datasets are collected in human-absent settings, and policies trained on such data may perform tasks competently in isolation but fail to exhibit human-aware behaviors. To address this gap, we introduce HABIT, a large-scale robot demonstration dataset for human-present environments. We organize tasks into three roles capturing distinct modes of human-robot interaction: Collaborator, where human and robot jointly accomplish a task; Coworker, where they pursue separate tasks in a shared space; and Supervisor, where the human directs the robot. The dataset comprises over 10K episodes and over 160 hours across 60 tasks. Our experiments show that training on human-present data elicits human-aware behaviors that robot-only data fails to produce: spatiotemporal synchronization in Collaborator tasks, yielding in Coworker tasks, and gesture grounding in Supervisor tasks. Moreover, training on HABIT enables rapid adaptation to new human-robot interaction tasks. By introducing human presence as a new axis of dataset diversity, HABIT extends robot policies to environments shared with humans.
Abstract (translated)
URL
https://arxiv.org/abs/2606.31682
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PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition
2026-06-30 08:45:55Yurui Liu, Xiao-Cong Zhong, Qisong Wang, Xuefu Wang, Dan Liu, Jinwei SunAbstract
Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction. However, its practical deployment remains challenging due to severe performance degradation caused by feature distribution discrepancies across different subjects and recording sessions. Although domain adaptation (DA) techniques are commonly employed to mitigate such discrepancies, conventional methods often struggle to effectively aligning sEMG features, primarily due to their inherent stochasticity and the scarcity of labeled data. To address these limitations, this paper proposes a novel Pressure-Guided Unsupervised Domain Adaptation (PGUDA) framework, which leverages the robustness and stability of pressure signals to introduce a cross-modal knowledge distillation strategy that transfers consistent physical semantics across modalities. Specifically, a teacher network trained on pressure signals guides an sEMG student network on unlabeled target domains, thereby regularizing the representation learning process with transferable and modality-invariant knowledge. Extensive experiments conducted on a self-collected multimodal dataset involving eleven subjects validate the effectiveness of the proposed PGUDA framework. The results demonstrate that our proposed PGUDA achieves leading performance in both cross-subject and cross-session classification tasks, achieving average accuracies of 58.08% and substantially outperforming existing DA approaches. Notably, PGUDA exhibits remarkable label efficiency: it attains classification accuracy comparable to fully supervised benchmarks while requiring only 5% of labeled data for teacher network training. This framework offers a robust and data-efficient solution that can significantly reduce the calibration burden in practical sEMG-based gesture recognition systems.
Abstract (translated)
URL
https://arxiv.org/abs/2606.31349
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LLM-Powered Interactive Robotic Action Synthesis from Multimodal Speech, Gestures, and Music
2026-06-30 05:35:26Snehasis Banerjee, Ranjan DasguptaAbstract
The quest for intuitive and natural human-robot interaction (HRI) remains a significant challenge in robotics. Traditional methods often rely on rigid, pre-programmed commands that limit the robot's expressiveness and adaptability. This paper introduces a novel framework that leverages the reasoning capabilities of Large Language Models (LLMs) to synthesize complex robotic actions from a rich tapestry of multimodal human inputs: natural speech, hand gestures, and music/sound beats. Our system architecture integrates a speech transcription model, a gesture recognition module, and a signal processing pipeline for beat detection. These processed inputs are contextualized using prompt templates and fed into a LLM. The LLM, informed by a predefined robot action space, reasons over the combined inputs to generate a coherent sequence of actions. This sequence is dispatched to an action queue for execution on a quadruped robot over ROS. The framework has ability to interpret and fuse semantic commands from speech, deictic information from gestures, and rhythmic cues from music. This work represents a step towards creating robots that can interact with humans in a more fluid, creative, and context-aware manner.
Abstract (translated)
URL
https://arxiv.org/abs/2606.31158
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