# Robot Behavior Personalization from Sparse User Feedback

arXiv: https://arxiv.org/abs/2410.19219 · HTML: https://mlsanity.com/paper/2410.19219/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 29 of 29)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [VIMA: General Robot Manipulation with Multimodal Prompts](https://mlsanity.com/paper/2210.03094/) (2022)
- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [Interactive Language: Talking to Robots in Real Time](https://mlsanity.com/paper/2210.06407/) (2022)
- [Open-vocabulary Queryable Scene Representations for Real World Planning](https://mlsanity.com/paper/2209.09874/) (2022)
- [Consequences of Misaligned AI](https://mlsanity.com/paper/2102.03896/) (2021)
- [Correcting Robot Plans with Natural Language Feedback](https://mlsanity.com/paper/2204.05186/) (2022)
- ["No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy](https://mlsanity.com/paper/2301.02555/) (2023)
- [The EMPATHIC Framework for Task Learning from Implicit Human Feedback](https://mlsanity.com/paper/2009.13649/) (2020)
- [Large Language Models as Zero-Shot Human Models for Human-Robot Interaction](https://mlsanity.com/paper/2303.03548/) (2023)
- [AvE: Assistance via Empowerment](https://mlsanity.com/paper/2006.14796/) (2020)
- [My House, My Rules: Learning Tidying Preferences with Graph Neural Networks](https://mlsanity.com/paper/2111.03112/) (2021)
- [Learning to Learn Faster from Human Feedback with Language Model Predictive Control](https://mlsanity.com/paper/2402.11450/) (2024)
- [Concept Learning for Interpretable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.12232/) (2023)
- [Proactive Robot Assistance via Spatio-Temporal Object Modeling](https://mlsanity.com/paper/2211.15501/) (2022)
- [State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding](https://mlsanity.com/paper/2309.12482/) (2023)
- [Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation](https://mlsanity.com/paper/2307.06333/) (2023)
- [Verifiably Following Complex Robot Instructions with Foundation Models](https://mlsanity.com/paper/2402.11498/) (2024)
- [Transformers are Adaptable Task Planners](https://mlsanity.com/paper/2207.02442/) (2022)
- [Coordination with Humans via Strategy Matching](https://mlsanity.com/paper/2210.15099/) (2022)
- [HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs](https://mlsanity.com/paper/2309.16524/) (2023)
- [ConSOR: A Context-Aware Semantic Object Rearrangement Framework for Partially Arranged Scenes](https://mlsanity.com/paper/2310.00371/) (2023)
- [Predicting Routine Object Usage for Proactive Robot Assistance](https://mlsanity.com/paper/2309.06252/) (2023)
- [Learning What To Do by Simulating the Past](https://mlsanity.com/paper/2104.03946/) (2021)
- [Explaining Autonomy: Enhancing Human-Robot Interaction through Explanation Generation with Large Language Models](https://mlsanity.com/paper/2402.04206/) (2024)

## Built on by (most cited first, 2 of 2)

- [Coloring Between the Lines: Personalization in the Null Space of Planning Constraints](https://mlsanity.com/paper/2505.15503/) (2025)
- [Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots](https://mlsanity.com/paper/2609.28910/) (2026)

## Abstract (arXiv)

As service robots become more general-purpose, they will need to adapt to their users' preferences over a large set of all possible tasks that they can perform. This includes preferences regarding which actions the users prefer to delegate to robots as opposed to doing themselves. Existing personalization approaches require task-specific data for each user. To handle diversity across all household tasks and users, and nuances in user preferences across tasks, we propose to learn a task adaptation function independently, which can be used in tandem with any universal robot policy to customize robot behavior. We create Task Adaptation using Abstract Concepts (TAACo) framework. TAACo can learn to predict the user's preferred manner of assistance with any given task, by mediating reasoning through a representation composed of abstract concepts built based on user feedback. TAACo can generalize to an open set of household tasks from small amount of user feedback and explain its inferences through intuitive concepts. We evaluate our model on a dataset we collected of 5 people's preferences, and show that TAACo outperforms GPT-4 by 16% and a rule-based system by 54%, on prediction accuracy, with 40 samples of user feedback.