Robi Butler: Multimodal Remote Interaction with a Household Robot Assistant

• URL: http://arxiv.org/abs/2409.20548v2
• Date: Mon, 10 Mar 2025 06:00:08 GMT
• Title: Robi Butler: Multimodal Remote Interaction with a Household Robot Assistant
• Abstract summary: Robi Butler is a new household robot assistant that enables seamless multimodal remote interaction.<n>It allows the human user to monitor its environment from a first-person view, issue voice or text commands, and specify target objects through hand-pointing gestures.
• Score: 19.28028454264679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imagine a future when we can Zoom-call a robot to manage household chores remotely. This work takes one step in this direction. Robi Butler is a new household robot assistant that enables seamless multimodal remote interaction. It allows the human user to monitor its environment from a first-person view, issue voice or text commands, and specify target objects through hand-pointing gestures. At its core, a high-level behavior module, powered by Large Language Models (LLMs), interprets multimodal instructions to generate multistep action plans. Each plan consists of open-vocabulary primitives supported by vision-language models, enabling the robot to process both textual and gestural inputs. Zoom provides a convenient interface to implement remote interactions between the human and the robot. The integration of these components allows Robi Butler to ground remote multimodal instructions in real-world home environments in a zero-shot manner. We evaluated the system on various household tasks, demonstrating its ability to execute complex user commands with multimodal inputs. We also conducted a user study to examine how multimodal interaction influences user experiences in remote human-robot interaction. These results suggest that with the advances in robot foundation models, we are moving closer to the reality of remote household robot assistants.

Related papers

• A Glimpse into Long-term Physical Coexistence with Intelligent Robots [12.275897522668858]
  PHILIA is a multi-robot agent built around a robot gateway abstraction.<n>It decouples low-frequency, high-semantic agent reasoning from high-frequency, low-level robot execution.<n>We present representative use cases in which agent memory and scene understanding are grounded in robot actions.
  arXiv  Detail & Related papers  (2026-07-13T10:40:32Z)
• Evaluation of Impression Difference of a Domestic Mobile Manipulator with Autonomous and/or Remote Control in Fetch-and-Carry Tasks [0.5833117322405447]
  A single service robot can present two distinct agencies: its onboard autonomy and an operator-mediated agency.<n>We formalize this dual-agency structure as a User-Robot-Operator triad in an autonomous remote-control setting.
  arXiv  Detail & Related papers  (2025-12-30T07:00:30Z)
• RoboOmni: Proactive Robot Manipulation in Omni-modal Context [165.09049429566238]
  We introduce cross-modal contextual instructions, a new setting where intent is derived from spoken dialogue, environmental sounds, and visual cues rather than explicit commands.<n>We present RoboOmni, a framework based on end-to-end omni-modal LLMs that unifies intention recognition, interaction confirmation, and action execution.<n>Experiments in simulation and real-world settings show that Robo Omni surpasses text- and ASR-based baselines in success rate, inference speed, intention recognition, and proactive assistance.
  arXiv  Detail & Related papers  (2025-10-27T18:49:03Z)
• Interpretable Robot Control via Structured Behavior Trees and Large Language Models [0.14990005092937678]
  This paper presents a novel framework that bridges natural language understanding and robotic execution.<n>The proposed approach is practical in real-world scenarios, with an average cognition-to-execution accuracy of approximately 94%.
  arXiv  Detail & Related papers  (2025-08-13T08:53:13Z)
• Autonomous Human-Robot Interaction via Operator Imitation [3.650193138379926]
  We propose to create autonomous interactive robots, by training a model to imitate operator data. Our model is trained on a dataset of human-robot interactions. We show that our method enables simple autonomous human-robot interactions comparable to the expert-operator baseline.
  arXiv  Detail & Related papers  (2025-04-03T16:06:44Z)
• Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents [23.960719833886984]
  M-CoDAL is a multimodal-dialogue system specifically designed for embodied agents to better understand and communicate in safety-critical situations. Our approach is evaluated using a newly created multimodal dataset comprising 1K safety violations extracted from 2K Reddit images. Results with this dataset demonstrate that our approach improves resolution of safety situations, user sentiment, as well as safety of the conversation.
  arXiv  Detail & Related papers  (2024-10-18T03:26:06Z)
• Robo-MUTUAL: Robotic Multimodal Task Specification via Unimodal Learning [35.42091835421386]
  Multimodal task specification is essential for enhanced robotic performance. We show that by leveraging unimodal instructions abundant in real data, we can effectively teach robots to learn multimodal task specifications.
  arXiv  Detail & Related papers  (2024-10-02T13:23:02Z)
• Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models [53.22792173053473]
  We introduce an interactive robotic manipulation framework called Polaris. Polaris integrates perception and interaction by utilizing GPT-4 alongside grounded vision models. We propose a novel Synthetic-to-Real (Syn2Real) pose estimation pipeline.
  arXiv  Detail & Related papers  (2024-08-15T06:40:38Z)
• Unifying 3D Representation and Control of Diverse Robots with a Single Camera [48.279199537720714]
  We introduce Neural Jacobian Fields, an architecture that autonomously learns to model and control robots from vision alone. Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot.
  arXiv  Detail & Related papers  (2024-07-11T17:55:49Z)
• ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning [74.58666091522198]
  We present a framework for intuitive robot programming by non-experts. We leverage natural language prompts and contextual information from the Robot Operating System (ROS) Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface.
  arXiv  Detail & Related papers  (2024-06-28T08:28:38Z)
• MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments [82.67236400004826]
  We introduce the Multimodal Embodied Interactive Agent (MEIA), capable of translating high-level tasks expressed in natural language into a sequence of executable actions. MEM module enables MEIA to generate executable action plans based on diverse requirements and the robot's capabilities.
  arXiv  Detail & Related papers  (2024-02-01T02:43:20Z)
• LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing [99.80742991922992]
  The system can have multi-turn dialogues with human users by taking multimodal user inputs and generating multimodal responses. LLaVA-Interactive goes beyond language prompt, where visual prompt is enabled to align human intents in the interaction.
  arXiv  Detail & Related papers  (2023-11-01T15:13:43Z)
• Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning [49.92517970237088]
  We tackle the problem of training a robot to understand multimodal prompts. This type of task poses a major challenge to robots' capability to understand the interconnection and complementarity between vision and language signals. We introduce an effective framework that learns a policy to perform robot manipulation with multimodal prompts.
  arXiv  Detail & Related papers  (2023-10-14T22:24:58Z)
• WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model [92.90127398282209]
  This paper investigates the potential of integrating the most recent Large Language Models (LLMs) and existing visual grounding and robotic grasping system. We introduce the WALL-E (Embodied Robotic WAiter load lifting with Large Language model) as an example of this integration. We deploy this LLM-empowered system on the physical robot to provide a more user-friendly interface for the instruction-guided grasping task.
  arXiv  Detail & Related papers  (2023-08-30T11:35:21Z)
• Giving Robots a Hand: Learning Generalizable Manipulation with Eye-in-Hand Human Video Demonstrations [66.47064743686953]
  Eye-in-hand cameras have shown promise in enabling greater sample efficiency and generalization in vision-based robotic manipulation. Videos of humans performing tasks, on the other hand, are much cheaper to collect since they eliminate the need for expertise in robotic teleoperation. In this work, we augment narrow robotic imitation datasets with broad unlabeled human video demonstrations to greatly enhance the generalization of eye-in-hand visuomotor policies.
  arXiv  Detail & Related papers  (2023-07-12T07:04:53Z)
• Affordances from Human Videos as a Versatile Representation for Robotics [31.248842798600606]
  We train a visual affordance model that estimates where and how in the scene a human is likely to interact. The structure of these behavioral affordances directly enables the robot to perform many complex tasks. We show the efficacy of our approach, which we call VRB, across 4 real world environments, over 10 different tasks, and 2 robotic platforms operating in the wild.
  arXiv  Detail & Related papers  (2023-04-17T17:59:34Z)
• Chat with the Environment: Interactive Multimodal Perception Using Large Language Models [19.623070762485494]
  Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. Our study demonstrates that LLMs can provide high-level planning and reasoning skills and control interactive robot behavior in a multimodal environment.
  arXiv  Detail & Related papers  (2023-03-14T23:01:27Z)
• Open-World Object Manipulation using Pre-trained Vision-Language Models [72.87306011500084]
  For robots to follow instructions from people, they must be able to connect the rich semantic information in human vocabulary. We develop a simple approach, which leverages a pre-trained vision-language model to extract object-identifying information. In a variety of experiments on a real mobile manipulator, we find that MOO generalizes zero-shot to a wide range of novel object categories and environments.
  arXiv  Detail & Related papers  (2023-03-02T01:55:10Z)
• Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos [59.58105314783289]
  Domain-agnostic Video Discriminator (DVD) learns multitask reward functions by training a discriminator to classify whether two videos are performing the same task. DVD can generalize by virtue of learning from a small amount of robot data with a broad dataset of human videos. DVD can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.
  arXiv  Detail & Related papers  (2021-03-31T05:25:05Z)
• Learning Multi-Arm Manipulation Through Collaborative Teleoperation [63.35924708783826]
  Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks. Many real-world tasks require multiple arms, such as lifting a heavy object or assembling a desk. We present Multi-Arm RoboTurk (MART), a multi-user data collection platform that allows multiple remote users to simultaneously teleoperate a set of robotic arms.
  arXiv  Detail & Related papers  (2020-12-12T05:43:43Z)

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.