VisionClaw: Always-On AI Agents through Smart Glasses

• URL: http://arxiv.org/abs/2604.03486v2
• Date: Wed, 08 Apr 2026 04:29:20 GMT
• Title: VisionClaw: Always-On AI Agents through Smart Glasses
• Abstract summary: We present VisionClaw, an always-on wearable AI agent that integrates live egocentric perception with agentic task execution.<n>Running on Meta Ray-Ban smart glasses, VisionClaw continuously perceives real-world context and enables in-situ, speech-driven action initiation and delegation.
• Score: 9.735400874849178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present VisionClaw, an always-on wearable AI agent that integrates live egocentric perception with agentic task execution. Running on Meta Ray-Ban smart glasses, VisionClaw continuously perceives real-world context and enables in-situ, speech-driven action initiation and delegation via OpenClaw AI agents. Therefore, users can directly execute tasks through the smart glasses, such as adding real-world objects to an Amazon cart, generating notes from physical documents, receiving meeting briefings on the go, creating events from posters, or controlling IoT devices. We evaluate VisionClaw through a controlled laboratory study (N=12) and a longitudinal deployment study (N=5). Results show that integrating perception and execution enables faster task completion and reduces interaction overhead compared to non-always-on and non-agent baselines. Beyond performance gains, deployment findings reveal a shift in interaction: tasks are initiated opportunistically during ongoing activities, and execution is increasingly delegated rather than manually controlled. These results suggest a new paradigm for wearable AI agents, where perception and action are continuously coupled to support situated, hands-free interaction.

Related papers

• Show-Harness: Just a VLM Agent Can Play Robots [53.80887083886227]
  Embodied vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging.<n>We present Show-Harness, an Embodied Harness that enablesVLMs to "play" robots through a compact semantic interface linking intent to action.<n>We further develop GUMI, which extends the same semantic action space to GUI-based demonstration collection, allowing humans and agents to "play" robots across embodiments without specialized teleoperation hardware.
  arXiv  Detail & Related papers  (2026-09-09T17:53:38Z)
• Towards Generalizable Visually Grounded Exploration of Household Devices [74.66117923766632]
  Key bottleneck of realizing general autonomous embodied agents lies in Generalizable Visually Grounded Exploration.<n>We introduce VGEBench, a benchmark designed to evaluate the generalizable visually grounded exploration capabilities of Vision-Language Models.<n>Unlike static datasets, we construct a Logic-Driven State Machine framework. This framework simulates multi-turn interaction loops, compelling agents to achieve goals by active visual perception and feedback-driven correction.
  arXiv  Detail & Related papers  (2026-09-01T07:42:45Z)
• An Augmented Reality Brain-Robot Interface for Generalist Robot Arm Manipulation [0.32326259807823027]
  We present an AR brain-robot interface (BRI) that combines gaze-based object selection with motor imagery action control.<n>Our system uses eye-tracking for intuitive object targeting and context-aware visual overlays to guide the user through tasks.
  arXiv  Detail & Related papers  (2026-06-15T08:50:43Z)
• Multi-Modal Multi-Agent Robotic Cognitive Alignment enabled by Non-Invasive Consumer Brain Computer Interfaces: A Proof of Concept Exploration [2.8348164438226537]
  This paper introduces a framework for generating "cognitively aligned" multi-agent interactions.<n>We present the design and implementation of a closed-loop architecture that explores the interplay between autonomous task execution and real-time neurophysiological focus.
  arXiv  Detail & Related papers  (2026-06-11T10:58:31Z)
• ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop [1.4279471205248535]
  ProcAgent is a vision-based procedural assistant for real-time adaptive guidances on a single NVIDIA Jetson AGX Orin.<n>We evaluate ProcAgent along four dimensions: perception accuracy, reasoning, task-level performance, and user experience.
  arXiv  Detail & Related papers  (2026-06-09T17:56:28Z)
• VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands [56.029505206531155]
  Vision Guided Agile Interaction Control (VAIC) is a unified framework that bridges the gap between current controllers and humanoid robots.<n>First, a privileged teacher policy masters diverse interaction skills using precise object kinematics and exact environmental states.<n>Second, a deployable student policy distills these capabilities by replacing full body tracking with velocity targets across multiple axes.
  arXiv  Detail & Related papers  (2026-06-08T09:52:55Z)
• ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration [51.69384671495837]
  ActiveGlasses is a system for learning robot manipulation from ego-centric human demonstrations with active vision.<n>A stereo camera mounted on smart glasses serves as the sole perception device for both data collection and policy inference.<n>To enable zero-transfer, we extract object trajectories from demonstrations and use an object-centric point-cloud policy to jointly predict manipulation and head movement.
  arXiv  Detail & Related papers  (2026-04-09T17:59:08Z)
• Egocentric Co-Pilot: Web-Native Smart-Glasses Agents for Assistive Egocentric AI [56.98603185789977]
  We present Egocentric Co-Pilot, a web-native neuro-symbolic framework that runs on smart glasses.<n>We use a Large Language Model (LLM) to orchestrate a toolbox of perception, reasoning, and web tools.<n> Experiments on Egolife and HD-EPIC demonstrate competitive or state-of-the-art egocentric QA performance.
  arXiv  Detail & Related papers  (2026-03-01T13:43:04Z)
• Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning [69.71072181304066]
  We introduce Perceptive Dexterous Control (PDC), a framework for vision-driven whole-body control with simulated humanoids.<n>PDC operates solely on egocentric vision for task specification, enabling object search, target placement, and skill selection through visual cues.<n>We show that training from scratch with reinforcement learning can produce emergent behaviors such as active search.
  arXiv  Detail & Related papers  (2025-05-18T07:33:31Z)
• Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach [23.52028824411467]
  We present a large-scale experimental study involving numepisodes navigation episodes in a real environment with a physical robot.<n>We analyze the type of reasoning emerging from end-to-end training.<n>We show in a post-hoc analysis that the value function learned by the agent relates to long-term planning.
  arXiv  Detail & Related papers  (2025-03-11T11:16:47Z)
• Object-Centric Latent Action Learning [70.3173534658611]
  We propose a novel object-centric latent action learning framework that centers on objects rather than pixels.<n>We leverage self-supervised object-centric pretraining to disentangle action-related and distracting dynamics.<n>Our results show that object-centric pretraining mitigates the negative effects of distractors by 50%.
  arXiv  Detail & Related papers  (2025-02-13T11:27:05Z)
• YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks [16.443149180969776]
  Augmented Reality (AR) head worn devices can uniquely improve the user experience of solving procedural day-to-day tasks.<n>Such AR capabilities can help AI Agents see and listen to actions that users take which can relate to multimodal capabilities of human users.<n>Proactivity of AI Agents on the other hand can help the human user detect and correct any mistakes in agent observed tasks.
  arXiv  Detail & Related papers  (2025-01-16T08:06:02Z)
• Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations [77.31328397965653]
  We introduce Ag2Manip (Agent-Agnostic representations for Manipulation), a framework aimed at surmounting challenges through two key innovations. A novel agent-agnostic visual representation derived from human manipulation videos, with the specifics of embodiments obscured to enhance generalizability. An agent-agnostic action representation abstracting a robot's kinematics to a universal agent proxy, emphasizing crucial interactions between end-effector and object.
  arXiv  Detail & Related papers  (2024-04-26T16:40:17Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.