Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models (2506.14727v1)

Abstract: Assistive teleoperation, where control is shared between a human and a robot, enables efficient and intuitive human-robot collaboration in diverse and unstructured environments. A central challenge in real-world assistive teleoperation is for the robot to infer a wide range of human intentions from user control inputs and to assist users with correct actions. Existing methods are either confined to simple, predefined scenarios or restricted to task-specific data distributions at training, limiting their support for real-world assistance. We introduce Casper, an assistive teleoperation system that leverages commonsense knowledge embedded in pre-trained visual LLMs (VLMs) for real-time intent inference and flexible skill execution. Casper incorporates an open-world perception module for a generalized understanding of novel objects and scenes, a VLM-powered intent inference mechanism that leverages commonsense reasoning to interpret snippets of teleoperated user input, and a skill library that expands the scope of prior assistive teleoperation systems to support diverse, long-horizon mobile manipulation tasks. Extensive empirical evaluation, including human studies and system ablations, demonstrates that Casper improves task performance, reduces human cognitive load, and achieves higher user satisfaction than direct teleoperation and assistive teleoperation baselines.

• The paper introduces CASPER, which employs VLMs to accurately infer diverse user intents, significantly enhancing assistive teleoperation performance.
• It integrates an open-world perception module and a comprehensive skill library to autonomously execute complex tasks in dynamic environments.
• Experimental evaluations reveal an 88.9% success rate with reduced task completion times and lower cognitive load for users.

Overview of "Inferring Diverse Intents for Assistive Teleoperation with Vision LLMs"

The paper "Inferring Diverse Intents for Assistive Teleoperation with Vision LLMs" explores the development of assistive teleoperation systems that integrate Vision LLMs (VLMs) to infer user intents in real-time and execute tasks with autonomy in dynamic and unstructured environments. This research addresses a critical challenge in assistive teleoperation—inferring human intentions accurately from control inputs to offer appropriate autonomous assistance.

Key Contributions

The system introduced in the paper, herein referred to as "CASPER," is distinctly designed to broaden the scope of assistive teleoperation beyond predefined scenarios. It employs VLMs to leverage commonsense reasoning for interpreting user inputs. The system achieves this through the following components:

1. Open-World Perception Module: This component is responsible for recognizing a variety of objects and contexts without task-specific training, thereby facilitating a generalized understanding critical for intent inference.
2. VLM-Powered Intent Inference: CASPER utilizes the capabilities of pre-trained VLMs to interpret teleoperation inputs based on visual context, thus expanding the diversity of intents it can recognize. It goes beyond static task-categorization by applying commonsense reasoning, allowing the system to predict user goals more accurately.
3. Comprehensive Skill Library: A library of parameterized skills is incorporated, enabling the execution of sophisticated tasks that involve navigation and manipulation, expanding beyond typical teleoperation capabilities.
4. Parallel Processing for Real-Time Interaction: The system operates by processing VLM-based inference in parallel with user control to minimize interference, ensuring efficient real-time operation.

Experimental Evaluation

The authors conducted extensive user studies and empirical evaluations that demonstrated the system's effectiveness. These studies highlighted the following outcomes:

• Task Performance: CASPER improved task success rates and reduced completion time compared to existing assistive teleoperation baselines and full manual teleoperation. It achieved an average success rate of 88.9%, surpassing baseline methods such as HAT and RBII that achieved success rates of 40.3% and 45.0%, respectively.
• User Experience: Participants reported lower cognitive load and higher satisfaction while using CASPER, as evaluated using NASA-TLX and user satisfaction metrics.

Implications and Future Directions

The paper underscores the potential of integrating VLMs in assistive teleoperation, enhancing the versatility and reliability of robotic assistance across various tasks. Practically, such systems could offer substantial benefits for individuals with physical impairments, promoting autonomy and ease of interaction in day-to-day tasks.

Looking forward, the paper suggests avenues for further research, such as developing ongoing learning capabilities to adaptively expand the skill set of the robot and enabling more nuanced intent inference in complex, real-world scenarios. Additionally, exploring robust uncertainty quantification techniques like conformal prediction could further refine the system's reliability.

In conclusion, this paper presents a significant step in advancing the domain of assistive robotics through a sophisticated integration of vision and LLMs, paving the way for robust human-robot collaboration in diverse settings.