VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots
- URL: http://arxiv.org/abs/2404.04066v2
- Date: Wed, 17 Jul 2024 01:38:16 GMT
- Title: VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots
- Authors: Akhil Padmanabha, Jessie Yuan, Janavi Gupta, Zulekha Karachiwalla, Carmel Majidi, Henny Admoni, Zackory Erickson,
- Abstract summary: Speech interfaces that utilize Large Language Models (LLMs) can enable individuals to communicate high-level commands and nuanced preferences to robots.
Frameworks for integrating LLMs as interfaces to robots for high level task planning and code generation have been proposed, but fail to incorporate human-centric considerations.
We present a framework for incorporating LLMs as speech interfaces for physically assistive robots, constructed iteratively with 3 stages of testing involving a feeding robot, culminating in an evaluation with 11 older adults at an independent living facility.
- Score: 9.528060348251584
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who are unable to complete activities of daily living. Speech interfaces, especially ones that utilize Large Language Models (LLMs), can enable individuals to effectively and naturally communicate high-level commands and nuanced preferences to robots. Frameworks for integrating LLMs as interfaces to robots for high level task planning and code generation have been proposed, but fail to incorporate human-centric considerations which are essential while developing assistive interfaces. In this work, we present a framework for incorporating LLMs as speech interfaces for physically assistive robots, constructed iteratively with 3 stages of testing involving a feeding robot, culminating in an evaluation with 11 older adults at an independent living facility. We use both quantitative and qualitative data from the final study to validate our framework and additionally provide design guidelines for using LLMs as speech interfaces for assistive robots. Videos and supporting files are located on our project website: https://sites.google.com/andrew.cmu.edu/voicepilot/
Related papers
- $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge.
We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
arXiv Detail & Related papers (2024-10-31T17:22:30Z) - Towards an LLM-Based Speech Interface for Robot-Assisted Feeding [9.528060348251584]
Speech interfaces that utilize Large Language Models (LLMs) can enable individuals to communicate high-level commands and nuanced preferences to robots.
In this work, we demonstrate an LLM-based speech interface for a commercially available assistive feeding robot.
arXiv Detail & Related papers (2024-10-27T22:56:51Z) - Interpreting and learning voice commands with a Large Language Model for a robot system [0.0]
The use of Large Language Models (LLMs) like GPT-4 has enhanced robot capabilities, allowing for real-time interaction and decision-making.
This project focuses on merging LLMs with databases to improve decision-making and enable knowledge acquisition for request interpretation problems.
arXiv Detail & Related papers (2024-07-31T10:30:31Z) - Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in
Conversations with the Tabletop Robot Haru [9.2526849536751]
We introduce a fully-automated conversation system that leverages large language models (LLMs) to generate robot responses with expressive behaviors.
We conduct a pilot study where volunteers chat with a social robot using our proposed system, and we analyze their feedback, conducting a rigorous error analysis of chat transcripts.
Most negative feedback was due to automatic speech recognition (ASR) errors which had limited impact on conversations.
arXiv Detail & Related papers (2024-02-18T12:35:52Z) - AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents [109.3804962220498]
AutoRT is a system to scale up the deployment of operational robots in completely unseen scenarios with minimal human supervision.
We demonstrate AutoRT proposing instructions to over 20 robots across multiple buildings and collecting 77k real robot episodes via both teleoperation and autonomous robot policies.
We experimentally show that such "in-the-wild" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences.
arXiv Detail & Related papers (2024-01-23T18:45:54Z) - Interactive Planning Using Large Language Models for Partially
Observable Robotics Tasks [54.60571399091711]
Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks.
We present an interactive planning technique for partially observable tasks using LLMs.
arXiv Detail & Related papers (2023-12-11T22:54:44Z) - Large Language Models for Robotics: A Survey [40.76581696885846]
Large language models (LLMs) possess the ability to process and generate natural language, facilitating efficient interaction and collaboration with robots.
This review aims to summarize the applications of LLMs in robotics, delving into their impact and contributions to key areas such as robot control, perception, decision-making, and path planning.
arXiv Detail & Related papers (2023-11-13T10:46:35Z) - Vision-Language Foundation Models as Effective Robot Imitators [48.73027330407576]
We derive a vision-language manipulation framework, dubbed RoboFlamingo, built upon the open-source VLMs, OpenFlamingo.
By exceeding the state-of-the-art performance with a large margin on the tested benchmark, we show RoboFlamingo can be an effective and competitive alternative to adapt VLMs to robot control.
arXiv Detail & Related papers (2023-11-02T16:34:33Z) - 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) - Language to Rewards for Robotic Skill Synthesis [37.21434094015743]
We introduce a new paradigm that harnesses large language models (LLMs) to define reward parameters that can be optimized and accomplish variety of robotic tasks.
Using reward as the intermediate interface generated by LLMs, we can effectively bridge the gap between high-level language instructions or corrections to low-level robot actions.
arXiv Detail & Related papers (2023-06-14T17:27:10Z) - Self-supervised reinforcement learning for speaker localisation with the
iCub humanoid robot [58.2026611111328]
Looking at a person's face is one of the mechanisms that humans rely on when it comes to filtering speech in noisy environments.
Having a robot that can look toward a speaker could benefit ASR performance in challenging environments.
We propose a self-supervised reinforcement learning-based framework inspired by the early development of humans.
arXiv Detail & Related papers (2020-11-12T18:02:15Z)
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.