Exploiting Socially-Aware Tasks for Embodied Social Navigation

• URL: http://arxiv.org/abs/2212.00767v1
• Date: Thu, 1 Dec 2022 18:52:46 GMT
• Title: Exploiting Socially-Aware Tasks for Embodied Social Navigation
• Authors: Enrico Cancelli, Tommaso Campari, Luciano Serafini, Angel X. Chang, Lamberto Ballan
• Abstract summary: We propose an end-to-end architecture that exploits Socially-Aware Tasks to inject into a reinforcement learning navigation policy. To this end, our tasks exploit the notion of immediate and future dangers of collision. We validate our approach on Gibson4+ and Habitat-Matterport3D datasets.
• Score: 17.48110264302196
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Learning how to navigate among humans in an occluded and spatially constrained indoor environment, is a key ability required to embodied agent to be integrated into our society. In this paper, we propose an end-to-end architecture that exploits Socially-Aware Tasks (referred as to Risk and Social Compass) to inject into a reinforcement learning navigation policy the ability to infer common-sense social behaviors. To this end, our tasks exploit the notion of immediate and future dangers of collision. Furthermore, we propose an evaluation protocol specifically designed for the Social Navigation Task in simulated environments. This is done to capture fine-grained features and characteristics of the policy by analyzing the minimal unit of human-robot spatial interaction, called Encounter. We validate our approach on Gibson4+ and Habitat-Matterport3D datasets.

Related papers

• 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)
• Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning [0.7864304771129751]
  Mobile robots are being used on a large scale in various crowded situations and become part of our society. Socially acceptable navigation behavior of a mobile robot with individual human consideration is an essential requirement for scalable applications and human acceptance. We propose a novel socially integrated navigation approach where the robot's social behavior is adaptive and emerges from the interaction with humans.
  arXiv  Detail & Related papers  (2024-03-14T18:25:40Z)
• Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
  Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments. It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion. Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
  arXiv  Detail & Related papers  (2023-10-19T17:29:17Z)
• SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
  We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans. In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
  arXiv  Detail & Related papers  (2023-10-18T02:27:01Z)
• 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)
• SocNavGym: A Reinforcement Learning Gym for Social Navigation [0.0]
  SocNavGym is an advanced simulation environment for social navigation. It can generate different types of social navigation scenarios. It can also be configured to work with different hand-crafted and data-driven social reward signals.
  arXiv  Detail & Related papers  (2023-04-27T11:29:02Z)
• Occlusion-Aware Crowd Navigation Using People as Sensors [8.635930195821263]
  Occlusions are highly prevalent in such settings due to a limited sensor field of view. Previous work has shown that observed interactive behaviors of human agents can be used to estimate potential obstacles. We propose integrating such social inference techniques into the planning pipeline.
  arXiv  Detail & Related papers  (2022-10-02T15:18:32Z)
• Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation [92.66286342108934]
  Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a'socially compliant' manner in the presence of other intelligent agents such as humans. Our dataset contains 8.7 hours, 138 trajectories, 25 miles of socially compliant, human teleoperated driving demonstrations.
  arXiv  Detail & Related papers  (2022-03-28T19:09:11Z)
• PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception [50.551003004553806]
  We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions. Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events. As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
  arXiv  Detail & Related papers  (2021-03-02T18:44:57Z)
• From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation [3.3511723893430476]
  We argue that social navigation models can replicate, promote, and amplify societal unfairness such as discrimination and segregation. Our proposed framework consists of two components: textitlearning which incorporates social context into the learning process to account for safety and comfort, and textitrelearning to detect and correct potentially harmful outcomes before the onset.
  arXiv  Detail & Related papers  (2021-01-07T17:42:35Z)

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.