# AvE: Assistance via Empowerment

arXiv: https://arxiv.org/abs/2006.14796 · HTML: https://mlsanity.com/paper/2006.14796/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2020.

## Built on by (most cited first, 27 of 27)

- [NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants](https://mlsanity.com/paper/2301.05223/) (2023)
- [To the Noise and Back: Diffusion for Shared Autonomy](https://mlsanity.com/paper/2302.12244/) (2023)
- [Learning to Assist Humans without Inferring Rewards](https://mlsanity.com/paper/2411.02623/) (2024)
- [Warmth and competence in human-agent cooperation](https://mlsanity.com/paper/2201.13448/) (2022)
- [Learning Altruistic Behaviours in Reinforcement Learning without External Rewards](https://mlsanity.com/paper/2107.09598/) (2021)
- [ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2202.02465/) (2022)
- [First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization](https://mlsanity.com/paper/2205.12381/) (2022)
- [Intrinsic Motivation in Dynamical Control Systems](https://mlsanity.com/paper/2301.00005/) (2022)
- [Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective](https://mlsanity.com/paper/2304.11632/) (2023)
- [Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain](https://mlsanity.com/paper/2401.16444/) (2024)
- [X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback](https://mlsanity.com/paper/2203.02072/) (2022)
- [Active Inference and Human--Computer Interaction](https://mlsanity.com/paper/2412.14741/) (2024)
- [On Optimizing Interventions in Shared Autonomy](https://mlsanity.com/paper/2112.09169/) (2021)
- [Shared Autonomy with IDA: Interventional Diffusion Assistance](https://mlsanity.com/paper/2409.15317/) (2024)
- [Estimating the Empowerment of Language Model Agents](https://mlsanity.com/paper/2509.22504/) (2025)
- [Training LLM Agents to Empower Humans](https://mlsanity.com/paper/2510.13709/) (2025)
- [Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventions](https://mlsanity.com/paper/2512.08230/) (2025)
- [Robot Behavior Personalization from Sparse User Feedback](https://mlsanity.com/paper/2410.19219/) (2024)
- [Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards](https://mlsanity.com/paper/2603.02008/) (2026)
- [Be Considerate: Objectives, Side Effects, and Deciding How to Act](https://mlsanity.com/paper/2106.02617/) (2021)
- [Latent-Predictive Empowerment: Measuring Empowerment without a Simulator](https://mlsanity.com/paper/2410.11155/) (2024)
- [Decentralized Traffic Flow Optimization Through Intrinsic Motivation](https://mlsanity.com/paper/2505.11520/) (2025)
- [Model-Based Soft Maximization of Suitable Metrics of Long-Term Human Power](https://mlsanity.com/paper/2508.00159/) (2025)
- [When Assisting One Disempowers Another](https://mlsanity.com/paper/2511.04177/) (2025)
- [Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation](https://mlsanity.com/paper/2606.01565/) (2026)
- [Balancing Performance and Human Autonomy with Implicit Guidance Agent](https://mlsanity.com/paper/2109.00414/) (2021)
- [Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning](https://mlsanity.com/paper/2309.03839/) (2023)

## Abstract (arXiv)

One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on inferring the human's goal, which is challenging when there are many potential goals or when the set of candidate goals is difficult to identify. We propose a new paradigm for assistance by instead increasing the human's ability to control their environment, and formalize this approach by augmenting reinforcement learning with human empowerment. This task-agnostic objective preserves the person's autonomy and ability to achieve any eventual state. We test our approach against assistance based on goal inference, highlighting scenarios where our method overcomes failure modes stemming from goal ambiguity or misspecification. As existing methods for estimating empowerment in continuous domains are computationally hard, precluding its use in real time learned assistance, we also propose an efficient empowerment-inspired proxy metric. Using this, we are able to successfully demonstrate our method in a shared autonomy user study for a challenging simulated teleoperation task with human-in-the-loop training.