Related papers: RILI: Robustly Influencing Latent Intent

RILI: Robustly Influencing Latent Intent

URL: http://arxiv.org/abs/2203.12705v1
Date: Wed, 23 Mar 2022 19:55:49 GMT
Title: RILI: Robustly Influencing Latent Intent
Authors: Sagar Parekh, Soheil Habibian, and Dylan P. Losey
Abstract summary: We propose a robust approach that learns to influence changing partner dynamics. Our method first trains with a set of partners across repeated interactions. We then rapidly adapt to new partners by sampling trajectories the robot learned with the original partners.
Score: 7.025418443146435
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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

Related papers

SACSoN: Scalable Autonomous Control for Social Navigation [62.59274275261392]
We develop methods for training policies for socially unobtrusive navigation. By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space. We collect a large dataset where an indoor mobile robot interacts with human bystanders.
arXiv Detail & Related papers (2023-06-02T19:07:52Z)
Learning Latent Representations to Co-Adapt to Humans [12.71953776723672]
Non-stationary humans are challenging for robot learners. In this paper we introduce an algorithmic formalism that enables robots to co-adapt alongside dynamic humans.
arXiv Detail & Related papers (2022-12-19T16:19:24Z)
Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks [59.098560311521034]
We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
arXiv Detail & Related papers (2022-03-29T15:03:05Z)
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer [57.045140028275036]
We consider the problem of transferring a policy across two different robots with significantly different parameters such as kinematics and morphology. Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being mismatched in different robots. We propose a novel method named $REvolveR$ of using continuous evolutionary models for robotic policy transfer implemented in a physics simulator.
arXiv Detail & Related papers (2022-02-10T18:50:25Z)
Physical Interaction as Communication: Learning Robot Objectives Online from Human Corrections [33.807697939765205]
Physical human-robot interaction (pHRI) is often intentional -- the human intervenes on purpose because the robot is not doing the task correctly. In this paper, we argue that when pHRI is intentional it is also informative: the robot can leverage interactions to learn how it should complete the rest of its current task even after the human lets go. Our results indicate that learning from pHRI leads to better task performance and improved human satisfaction.
arXiv Detail & Related papers (2021-07-06T02:25:39Z)
A proxemics game between festival visitors and an industrial robot [1.2599533416395767]
Nonverbal behaviours of collaboration partners in human-robot teams influence the experience of the human interaction partners. During the Ars Electronica 2020 Festival for Art, Technology and Society (Linz, Austria), we invited visitors to interact with an industrial robot. We investigated general nonverbal behaviours of the humans interacting with the robot, as well as nonverbal behaviours of people in the audience.
arXiv Detail & Related papers (2021-05-28T13:26:00Z)
Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
arXiv Detail & Related papers (2021-03-06T09:14:30Z)
Affect-Driven Modelling of Robot Personality for Collaborative Human-Robot Interactions [16.40684407420441]
Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour. We propose a novel framework for personality-driven behaviour generation in social robots.
arXiv Detail & Related papers (2020-10-14T16:34:14Z)
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks [83.37025218216888]
We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations. The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications. Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
arXiv Detail & Related papers (2020-07-24T23:35:03Z)
Social Navigation with Human Empowerment driven Deep Reinforcement Learning [20.815007485176615]
The next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. In this paper, we go beyond the approach of classical acfRL and provide our agent with intrinsic motivation using empowerment. Our approach has a positive influence on humans, as it minimizes its distance to humans and thus decreases human travel time while moving efficiently towards its own goal.
arXiv Detail & Related papers (2020-03-18T11:16:07Z)
Human Grasp Classification for Reactive Human-to-Robot Handovers [50.91803283297065]
We propose an approach for human-to-robot handovers in which the robot meets the human halfway. We collect a human grasp dataset which covers typical ways of holding objects with various hand shapes and poses. We present a planning and execution approach that takes the object from the human hand according to the detected grasp and hand position.
arXiv Detail & Related papers (2020-03-12T19:58:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.