# Advancing Household Robotics: Deep Interactive Reinforcement Learning for Efficient Training and Enhanced Performance

arXiv: https://arxiv.org/abs/2405.18687 · HTML: https://mlsanity.com/paper/2405.18687/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 81 references that are in the map.
- First posted on arXiv 29 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 7 of 7)

- [A Comprehensive AI Policy Education Framework for University Teaching and Learning](https://mlsanity.com/paper/2305.00280/) (2023)
- [From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility](https://mlsanity.com/paper/2402.16142/) (2024)
- [MLOps: A Review](https://mlsanity.com/paper/2308.10908/) (2023)
- [From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue](https://mlsanity.com/paper/2311.16958/) (2023)
- [Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2306.02308/) (2023)
- [Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19](https://mlsanity.com/paper/2401.15675/) (2024)
- [Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability](https://mlsanity.com/paper/2401.18040/) (2024)

## Built on by (most cited first, 4 of 4)

- [Optimizing Photometric Light Curve Analysis: Evaluating Scipy's Minimize Function for Eclipse Mapping of Cataclysmic Variables](https://mlsanity.com/paper/2406.00071/) (2024)
- [Multi-scale Temporal Prediction via Incremental Generation and Multi-agent Collaboration](https://mlsanity.com/paper/2509.17429/) (2025)
- [Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters](https://mlsanity.com/paper/2604.15339/) (2026)
- [Memory Tree Guided Key Frame Querying for Efficient 3D Question Answering](https://mlsanity.com/paper/2608.18009/) (2026)

## Abstract (arXiv)

The market for domestic robots made to perform household chores is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labor, in contrast to industrial robots, which are frequently criticized for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in several minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviors. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to optimize their actions to maximize rewards. However, the goal of Deep Reinforcement Learning is to address more complicated, continuous action-state spaces in real-world settings by combining RL with Neural Networks. The efficacy of DeepRL can be further augmented through interactive feedback, in which a trainer offers real-time guidance to expedite the robot's learning process. Nevertheless, the current methods have drawbacks, namely the transient application of guidance that results in repeated learning under identical conditions. Therefore, we present a novel method to preserve and reuse information and advice via Deep Interactive Reinforcement Learning, which utilizes a persistent rule-based system. This method not only expedites the training process but also lessens the number of repetitions that instructors will have to carry out. This study has the potential to advance the development of household robots and improve their effectiveness and efficiency as learners.