AutoPal: Autonomous Adaptation to Users for Personal AI Companionship

• URL: http://arxiv.org/abs/2406.13960v3
• Date: Fri, 18 Oct 2024 03:10:13 GMT
• Title: AutoPal: Autonomous Adaptation to Users for Personal AI Companionship
• Authors: Yi Cheng, Wenge Liu, Kaishuai Xu, Wenjun Hou, Yi Ouyang, Chak Tou Leong, Xian Wu, Yefeng Zheng,
• Abstract summary: This paper emphasizes the necessity of autonomous adaptation in personal AI companionship. We devise a hierarchical framework, AutoPal, that enables controllable and authentic adjustments to the agent's persona. Experiments demonstrate the effectiveness of AutoPal and highlight the importance of autonomous adaptability in AI companionship.
• Score: 39.03695909247373
• License:
• Abstract: Previous research has demonstrated the potential of AI agents to act as companions that can provide constant emotional support for humans. In this paper, we emphasize the necessity of autonomous adaptation in personal AI companionship, an underexplored yet promising direction. Such adaptability is crucial as it can facilitate more tailored interactions with users and allow the agent to evolve in response to users' changing needs. However, imbuing agents with autonomous adaptability presents unique challenges, including identifying optimal adaptations to meet users' expectations and ensuring a smooth transition during the adaptation process. To address them, we devise a hierarchical framework, AutoPal, that enables controllable and authentic adjustments to the agent's persona based on user interactions. A personamatching dataset is constructed to facilitate the learning of optimal persona adaptations. Extensive experiments demonstrate the effectiveness of AutoPal and highlight the importance of autonomous adaptability in AI companionship.

Related papers

• A Framework for Adapting Human-Robot Interaction to Diverse User Groups [16.17512394063696]
  We present a novel framework for adaptive Human-Robot Interaction (HRI) Our primary contributions include the development of an adaptive, ROS-based HRI framework with an open-source code base. This framework supports natural interactions through advanced speech recognition and voice activity detection.
  arXiv  Detail & Related papers  (2024-10-15T08:16:43Z)
• Smart Help: Strategic Opponent Modeling for Proactive and Adaptive Robot Assistance in Households [30.33911147366425]
  Smart Help aims to provide proactive yet adaptive support to human agents with diverse disabilities. We introduce an innovative opponent modeling module that provides a nuanced understanding of the main agent's capabilities and goals. Our findings illustrate the potential of AI-imbued assistive robots in improving the well-being of vulnerable groups.
  arXiv  Detail & Related papers  (2024-04-13T13:03:59Z)
• Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation [0.6591036379613505]
  We develop computational modeling and optimization techniques for enhancing the performance of suboptimal human-agent teams. We adopt an online Bayesian approach that enables a robot to infer people's willingness to comply with its assistance in a sequential decision-making game. Our user studies show that user preferences and team performance indeed vary with robot intervention styles.
  arXiv  Detail & Related papers  (2024-03-24T14:38:18Z)
• User-Driven Adaptation: Tailoring Autonomous Driving Systems with Dynamic Preferences [8.555603201531646]
  This study focuses on aligning system behavior with user expectations through feedback-driven adaptation. The findings affirm the approach's ability to effectively merge algorithm-driven adjustments with user complaints.
  arXiv  Detail & Related papers  (2024-03-05T12:44:54Z)
• AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems [112.76941157194544]
  We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
  arXiv  Detail & Related papers  (2023-10-13T16:37:14Z)
• "No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy [70.45420918526926]
  We present LILAC, a framework for incorporating and adapting to natural language corrections online during execution. Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot. We show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users.
  arXiv  Detail & Related papers  (2023-01-06T15:03:27Z)
• Safe adaptation in multiagent competition [48.02377041620857]
  In multiagent competitive scenarios, ego-agents may have to adapt to new opponents with previously unseen behaviors. As the ego-agent updates its own behavior to exploit the opponent, its own behavior could become more exploitable. We develop a safe adaptation approach in which the ego-agent is trained against a regularized opponent model.
  arXiv  Detail & Related papers  (2022-03-14T23:53:59Z)
• Self-Initiated Open World Learning for Autonomous AI Agents [16.41396764793912]
  As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous. This paper proposes a theoretic framework for this learning paradigm to promote the research of building Self-initiated Open world Learning agents.
  arXiv  Detail & Related papers  (2021-10-21T18:11:02Z)
• Learning Compliance Adaptation in Contact-Rich Manipulation [81.40695846555955]
  We propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller.
  arXiv  Detail & Related papers  (2020-05-01T05:23:34Z)
• SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning [61.335252950832256]
  Sense-Plan-Ask, or SPA, generates plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. We find that our algorithm creates a small runtime cost and enables agents to complete their goals more effectively than agents without the ability to leverage natural-language communication.
  arXiv  Detail & Related papers  (2020-02-08T23:15:06Z)

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.