Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing

• URL: http://arxiv.org/abs/2410.10062v1
• Date: Mon, 14 Oct 2024 01:00:46 GMT
• Title: Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing
• Authors: Jonathan DeCastro, Andrew Silva, Deepak Gopinath, Emily Sumner, Thomas M. Balch, Laporsha Dees, Guy Rosman,
• Abstract summary: Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions. We present Dream2Assist, a framework that combines a rich world model able to infer human objectives and value functions. We show that the combined human-robot team, when blending its actions with those of the human, outperforms the synthetic humans alone.
• Score: 10.947581892636629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates must react to cues of a human teammate's tactical objective to assist in a way that is consistent with the objective (e.g., navigating left or right around an obstacle). To address this challenge, we present Dream2Assist, a framework that combines a rich world model able to infer human objectives and value functions, and an assistive agent that provides appropriate expert assistance to a given human teammate. Our approach builds on a recurrent state space model to explicitly infer human intents, enabling the assistive agent to select actions that align with the human and enabling a fluid teaming interaction. We demonstrate our approach in a high-speed racing domain with a population of synthetic human drivers pursuing mutually exclusive objectives, such as "stay-behind" and "overtake". We show that the combined human-robot team, when blending its actions with those of the human, outperforms the synthetic humans alone as well as several baseline assistance strategies, and that intent-conditioning enables adherence to human preferences during task execution, leading to improved performance while satisfying the human's objective.

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)
• CoNav: A Benchmark for Human-Centered Collaborative Navigation [66.6268966718022]
  We propose a collaborative navigation (CoNav) benchmark. Our CoNav tackles the critical challenge of constructing a 3D navigation environment with realistic and diverse human activities. We propose an intention-aware agent for reasoning both long-term and short-term human intention.
  arXiv  Detail & Related papers  (2024-06-04T15:44:25Z)
• Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation [0.6591036379613505]
  We develop computational modeling and optimization techniques for enhancing the performance of suboptimal human-agent teams. We adopt an online Bayesian approach that enables a robot to infer people's willingness to comply with its assistance in a sequential decision-making game. Our user studies show that user preferences and team performance indeed vary with robot intervention styles.
  arXiv  Detail & Related papers  (2024-03-24T14:38:18Z)
• The role of haptic communication in dyadic collaborative object manipulation tasks [6.46682752231823]
  We investigate the role of haptics in human collaborative physical tasks. We present a task to balance a ball at a target position on a board. We find that humans can better coordinate with one another when haptic feedback is available.
  arXiv  Detail & Related papers  (2022-03-02T18:13:54Z)
• 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)
• Forming Human-Robot Cooperation for Tasks with General Goal using Evolutionary Value Learning [9.053709318841232]
  In Human-Robot Cooperation (HRC), the robot cooperates with humans to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and the robot infers and acts toward it. We present the Evolutionary Value Learning (EVL) approach to model the dynamics of the goal specification process in HRC.
  arXiv  Detail & Related papers  (2020-12-19T20:27:09Z)
• 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)
• AvE: Assistance via Empowerment [77.08882807208461]
  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.
  arXiv  Detail & Related papers  (2020-06-26T04:40:11Z)
• 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.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.