AvE: Assistance via Empowerment

• URL: http://arxiv.org/abs/2006.14796v5
• Date: Thu, 7 Jan 2021 20:54:48 GMT
• Title: AvE: Assistance via Empowerment
• Authors: Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca Dragan
• Abstract summary: We propose a new paradigm for assistance by instead increasing the human's ability to control their environment. This task-agnostic objective preserves the person's autonomy and ability to achieve any eventual state.
• Score: 77.08882807208461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Learning to Assist Humans without Inferring Rewards [65.28156318196397]
  We build upon prior work that studies assistance through the lens of empowerment. An assistive agent aims to maximize the influence of the human's actions. We prove that these representations estimate a similar notion of empowerment to that studied by prior work.
  arXiv  Detail & Related papers  (2024-11-04T21:31:04Z)
• 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)
• NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants [79.27554831580309]
  We study how to build socially intelligent robots to assist people in their homes. We focus on assistance with online goal inference, where robots must simultaneously infer humans' goals.
  arXiv  Detail & Related papers  (2023-01-12T18:59:34Z)
• Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration [68.94506047556412]
  We propose to leverage a sequential bias to learn control policies for complex robotic tasks using a single demonstration. We show that DCIL-II can solve with unprecedented sample efficiency some challenging simulated tasks such as humanoid locomotion and stand-up.
  arXiv  Detail & Related papers  (2022-11-09T10:28:40Z)
• Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment [52.07473934146584]
  We guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process. Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications. It shows reinforcement learning performance can successfully adjust in sync with the human desired difficulty level.
  arXiv  Detail & Related papers  (2022-08-04T23:53:51Z)
• Automatic Goal Generation using Dynamical Distance Learning [5.797847756967884]
  Reinforcement Learning (RL) agents can learn to solve complex sequential decision making tasks by interacting with the environment. In the field of multi-goal RL, where agents are required to reach multiple goals to solve complex tasks, improving sample efficiency can be especially challenging. We propose a method for automatic goal generation using a dynamical distance function (DDF) in a self-supervised fashion.
  arXiv  Detail & Related papers  (2021-11-07T16:23:56Z)
• Automatic Curriculum Learning through Value Disagreement [95.19299356298876]
  Continually solving new, unsolved tasks is the key to learning diverse behaviors. In the multi-task domain, where an agent needs to reach multiple goals, the choice of training goals can largely affect sample efficiency. We propose setting up an automatic curriculum for goals that the agent needs to solve. We evaluate our method across 13 multi-goal robotic tasks and 5 navigation tasks, and demonstrate performance gains over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-17T03:58:25Z)

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.