Autonomous navigation in dynamic social environments using Multi-Policy Decision Making

Mehta, Dhanvin; Ferrer, Gonzalo; Olson, Edwin

doi:10.1109/iros.2016.7759200

Cited by 68 publications

(50 citation statements)

References 24 publications

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“…Existing work on cooperative, socially compliant navigation can be broadly classified into two categories, namely model-based and learning-based. Model-based approaches are typically extensions of multiagent collision avoidance algorithms, with additional parameters introduced to account for social interactions [7], [10]- [13]. For instance, to distinguish between human-human and human-robot interactions, the extended social forces model [11], [12] augments the potential field algorithm with additional terms that specify the repulsive forces (e.g., strength and range) governing each type of interaction.…”

Section: Introductionmentioning

confidence: 99%

Socially aware motion planning with deep reinforcement learning

Chen

Everett

Liu

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

598

337

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Abstract-For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.

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Section: Introductionmentioning

confidence: 99%

Socially aware motion planning with deep reinforcement learning

Chen

Everett

Liu

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

598

337

View full text Add to dashboard Cite

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“…The concept of modality shifting in human-aware navigation is discussed in works by Mehta et al [6] and Qian et al [5], where Partially Observable Markov Decision Process (POMDP) is used for decision making. In both of the works, different modalities necessary for human-aware navigation are proposed, assuming that the robot takes all the load of the navigation process.…”

Section: Related Workmentioning

confidence: 99%

“…With the increasing complexity in environments and the need to navigate robots in such environments, decision making has been introduced into planning [5], [6]. However, these frameworks might make the robot wait in confined spaces instead of proactively planning and hence resulting in larger execution times.…”

Section: Introductionmentioning

confidence: 99%

HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

Alami

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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We propose a new framework combining decision making and planning in the human-robot co-navigation scenario. This new framework, called HATEB-2, introduces different modalities of planning and shift between them based on the situation at hand. These transitions are controlled by the decision making loop present on top of the planning. We also present the improvements made to human prediction and estimation along with the modifications to a few social constraints from our previous work, that are included in HATEB-2. Finally, several experiments are performed in human-robot co-navigation scenarios and results are presented. One of the modalities of HATEB-2 is used in EU-funded MuMMER [1] project (http://mummer-project.eu/).

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“…Other works focus on improving the simultaneous localization and navigation through the integration of some a priori knowledge, e.g. some pre-analyzed human behaviors [63,70,77,102] or knowledge from some external sources [7,65,80].…”

Section: Mobility In Urban Dynamic Environmentsmentioning

confidence: 99%

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

Tiddi

Bastianelli²,

Daga

et al. 2019

Int J of Soc Robotics

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The goal of this work is to describe how robots interact with complex city environments, and to identify the main characteristics of an emerging field that we call Robot-City Interaction (RCI). Given the central role recently gained by modern cities as use cases for the deployment of advanced technologies, and the advancements achieved in the robotics field in recent years, we assume that there is an increasing interest both in integrating robots in urban ecosystems, and in studying how they can interact and benefit from each others. Therefore, our challenge becomes to verify the emergence of such area, to assess its current state and to identify the main characteristics, core themes and research challenges associated with it. This is achieved by reviewing a preliminary body of work contributing to this area, which we classify and analyze according to an analytical framework including a set of key dimensions for the area of RCI. Such review not only serves as a preliminary state-of-the-art in the area, but also allows us to identify the main characteristics of RCI and its research landscape.

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Autonomous navigation in dynamic social environments using Multi-Policy Decision Making

Cited by 68 publications

References 24 publications

Socially aware motion planning with deep reinforcement learning

Socially aware motion planning with deep reinforcement learning

HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

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