mlsanityData to 7 Oct 2026

RILI: Robustly Influencing Latent Intent

arXiv 2203.12705 ↗First posted 23 Mar 2022cs.RO cs.AIcited by 8

Not on the map. Why?

Abstract (arXiv)

When robots interact with human partners, often these partners change their behavior in response to the robot. On the one hand this is challenging because the robot must learn to coordinate with a dynamic partner. But on the other hand -- if the robot understands these dynamics -- it can harness its own behavior, influence the human, and guide the team towards effective collaboration. Prior research enables robots to learn to influence other robots or simulated agents. In this paper we extend these learning approaches to now influence humans. What makes humans especially hard to influence is that -- not only do humans react to the robot -- but the way a single user reacts to the robot may change over time, and different humans will respond to the same robot behavior in different ways. We therefore propose a robust approach that learns to influence changing partner dynamics. Our method first trains with a set of partners across repeated interactions, and learns to predict the current partner's behavior based on the previous states, actions, and rewards. Next, we rapidly adapt to new partners by sampling trajectories the robot learned with the original partners, and then leveraging those existing behaviors to influence the new partner dynamics. We compare our resulting algorithm to state-of-the-art baselines across simulated environments and a user study where the robot and participants collaborate to build towers. We find that our approach outperforms the alternatives, even when the partner follows new or unexpected dynamics. Videos of the user study are available here: https://youtu.be/lYsWM8An18g

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

16 of 25 references.

References · 16

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
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  3. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  4. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 345
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  6. 2021Collaborating with Humans without Human Datacited by 139
  7. 2017A Survey of Learning in Multiagent Environments: Dealing with Non-Stationaritycited by 93
  8. 2020Learning Latent Representations to Influence Multi-Agent Interactioncited by 92
  9. 2013Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizationscited by 68
  10. 2018Emergent Communication through Negotiationcited by 62
  11. 2020Emergent Social Learning via Multi-agent Reinforcement Learningcited by 44
  12. 2021On the Critical Role of Conventions in Adaptive Human-AI Collaborationcited by 26
4 more
  1. 2021Influencing Towards Stable Multi-Agent Interactionscited by 24
  2. 2020Adversarially Guided Self-Play for Adopting Social Conventionscited by 10
  3. 2019Robots that Take Advantage of Human Trustcited by 3
  4. 2022Communicating Robot Conventions through Shared Autonomycited by 1

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 5; 2024: 1.

8 papers.

  1. 2022Learning Representations that Enable Generalization in Assistive Taskscited by 11
  2. 2023Learning Vision-based Pursuit-Evasion Robot Policiescited by 9
  3. 2024Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowdscited by 6
  4. 2022Learning Latent Representations to Co-Adapt to Humanscited by 5
  5. 2023iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learningcited by 5
  6. 2023PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systemscited by 4
  7. 2023Towards Co-operative Congestion Mitigationcited by 3
  8. 2023A behavioural transformer for effective collaboration between a robot and a non-stationary humancited by 2