Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph

• URL: http://arxiv.org/abs/2203.01821v4
• Date: Mon, 24 Apr 2023 20:40:16 GMT
• Title: Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
• Authors: Shuijing Liu, Peixin Chang, Zhe Huang, Neeloy Chakraborty, Kaiwen Hong, Weihang Liang, D. Livingston McPherson, Junyi Geng, and Katherine Driggs-Campbell
• Abstract summary: We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. We propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents. We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios.
• Score: 3.8461692052415137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation. To learn a safe and efficient robot policy, we propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents through space and time. To encourage longsighted robot behaviors, we infer the intentions of dynamic agents by predicting their future trajectories for several timesteps. The predictions are incorporated into a model-free RL framework to prevent the robot from intruding into the intended paths of other agents. We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios. We successfully transfer the policy learned in simulation to a real-world TurtleBot 2i. Our code and videos are available at https://sites.google.com/view/intention-aware-crowdnav/home.

Related papers

• HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments [8.974071308749007]
  We study the problem of robot navigation in dense and interactive crowds with environmental constraints such as corridors and furniture. Previous methods fail to consider all types of interactions among agents and obstacles, leading to unsafe and inefficient robot paths. We propose a structured framework to learn robot navigation policies with reinforcement learning.
  arXiv  Detail & Related papers  (2024-11-19T00:56:35Z)
• Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning [0.0]
  We present a novel methodology that enhances the robot's interaction with different types of agents and obstacles. This approach uses information about the entity types, improving collision avoidance and ensuring safer navigation. We introduce a new reward function that penalizes the robot for collisions with different entities such as adults, bicyclists, children, and static obstacles.
  arXiv  Detail & Related papers  (2024-08-26T11:16:03Z)
• What Matters to You? Towards Visual Representation Alignment for Robot Learning [81.30964736676103]
  When operating in service of people, robots need to optimize rewards aligned with end-user preferences. We propose Representation-Aligned Preference-based Learning (RAPL), a method for solving the visual representation alignment problem.
  arXiv  Detail & Related papers  (2023-10-11T23:04:07Z)
• Learning Vision-based Pursuit-Evasion Robot Policies [54.52536214251999]
  We develop a fully-observable robot policy that generates supervision for a partially-observable one. We deploy our policy on a physical quadruped robot with an RGB-D camera on pursuit-evasion interactions in the wild.
  arXiv  Detail & Related papers  (2023-08-30T17:59:05Z)
• 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)
• Affordances from Human Videos as a Versatile Representation for Robotics [31.248842798600606]
  We train a visual affordance model that estimates where and how in the scene a human is likely to interact. The structure of these behavioral affordances directly enables the robot to perform many complex tasks. We show the efficacy of our approach, which we call VRB, across 4 real world environments, over 10 different tasks, and 2 robotic platforms operating in the wild.
  arXiv  Detail & Related papers  (2023-04-17T17:59:34Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• Gesture2Path: Imitation Learning for Gesture-aware Navigation [54.570943577423094]
  We present Gesture2Path, a novel social navigation approach that combines image-based imitation learning with model-predictive control. We deploy our method on real robots and showcase the effectiveness of our approach for the four gestures-navigation scenarios.
  arXiv  Detail & Related papers  (2022-09-19T23:05:36Z)
• Multi-subgoal Robot Navigation in Crowds with History Information and Interactions [0.0]
  Multi-subgoal robot navigation approach based on deep reinforcement learning is proposed. Next position point is planned for the robot by introducing history information and interactions in our work. Experiments demonstrate that our approach outperforms state-of-the-art approaches in terms of both success rate and collision rate.
  arXiv  Detail & Related papers  (2022-05-04T11:24:49Z)
• 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)
• Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning [4.724825031148412]
  We propose structural-Recurrent Neural Network (DS-RNN), a novel network that reasons about spatial and temporal relationships for robot decision making in crowd navigation. We demonstrate that our model outperforms previous methods in challenging crowd navigation scenarios. We successfully transfer the policy learned in the simulator to a real-world TurtleBot 2i.
  arXiv  Detail & Related papers  (2020-11-09T23:15:31Z)

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.