Socially compliant mobile robot navigation via inverse reinforcement learning

Kretzschmar, Henrik; Spies, Markus; Sprunk, Christoph; Burgard, Wolfram

doi:10.1177/0278364915619772

Cited by 391 publications

(277 citation statements)

References 49 publications

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“…Model cooperation via joint distributions, i.e., essentially modeling the robot as one of the other agents. Examples are joint probability distributions (85) and joint cost distributions (88).…”

Section: Cooperation and Interactionmentioning

confidence: 99%

Planning and Decision-Making for Autonomous Vehicles

Schwarting

Alonso–Mora

Rus

2018

Annu. Rev. Control Robot. Auton. Syst.

668

378

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In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and systemcompliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles. 8.1

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Section: Cooperation and Interactionmentioning

confidence: 99%

Planning and Decision-Making for Autonomous Vehicles

Schwarting

Alonso–Mora

Rus

2018

Annu. Rev. Control Robot. Auton. Syst.

668

378

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“…Based on these works, many IRL based prediction or human behavior modeling approaches have been proposed. For instance, [21] learned a model for cooperative agents to generate human navigation behavior, and in [22], the authors used IRL to model the social impact between interactive agents. In [23], the authors formulated a hierarchical IRL to model human driver's decision making and trajectory planning, so that interactive driving behaviors can be predicted.…”

Section: A Motivationmentioning

confidence: 99%

Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors

Sun

Tomizuka

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Accurately predicting future behaviors of surrounding vehicles is an essential capability for autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others, however, are full of uncertainties. Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module. The prediction module is also expected to generate reasonable results in the presence of unseen and corner scenarios. Two types of prediction models are typically used to solve the prediction problem: learning-based model and planning-based model. Learning-based model utilizes real driving data to model the human behaviors. Depending on the structure of the data, learning-based models can predict both rational and irrational behaviors. But the balance between them cannot be customized, which creates challenges in generalizing the prediction results. Planning-based model, on the other hand, usually assumes human as a rational agent, i.e., it anticipates only rational behavior of human drivers. In this paper, a generic prediction architecture is proposed to address various rationalities in human behavior. We leverage the advantages from both learningbased and planning-based prediction models. The proposed approach is able to predict continuous trajectories that wellreflect possible future situations of other drivers. Moreover, the prediction performance remains stable under various unseen driving scenarios. A case study under a real-world roundabout scenario is provided to demonstrate the performance and capability of the proposed prediction architecture.

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“…As was proved mathematically, unless crowd navigation is treated as joint decision making, the robot suffers the freezing robot problem (FRP). The FRP has been experimentally observed in independent studies [54], [37]: beyond 0.55 people/m 2 , the robot was unable to move, and a 3x improvement in safety was observed when crowd navigation was treated as joint decision making instead of as path planning.…”

Section: A Robot Crowd Navigation As a Joint Decision Making Problemmentioning

confidence: 99%

Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction

Trautman

2017

Robotics: Science and Systems XIII

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Abstract-Human machine teaming has, for decades, been conceptualized as a function allocation (FA) or levels of autonomy (LOA) process: the human is suited for some tasks, while the machine is suitable for others, and as machines improve they take over duties previously assigned to humans. A wide variety of methods-including adaptive, adjustable, blended, supervisory and mixed initiative control, implemented discretely or continuously, as potential fields, as virtual fixture interfaces, or haptic interfaces-are derivatives of FA/LOA. We formalize FA/LOA (and all their derivatives) under a single mathematical formulation called classical shared control (CSC). Despite the widespread adoption of CSC, we prove that it fails to optimize human and robot agreement and intent if either the human or robot model displays "intention ambiguity" (e.g., the human's intended goal is unclear or the robot finds multiple viable solutions). Practically, this suboptimality can manifest as unnecessary and unresolvable disagreement (an unnecessary deadlock). For instance, if the robot chooses to go left around an obstacle and the human chooses to go right, CSC only provides two solutions: freeze in place or collide with the obstacle (we provide a wide variety of failure examples in [52], https://arxiv.org/abs/1611.09490). We find that CSC suboptimality stems from arbitrating over model samples, rather than over models. Our key insight is thus to arbitrate over human and robot distributions; we prove this method optimizes human and robot agreement and intent and resolves deadlocking. Our key contribution is computationally efficient distribution arbitration: if the human and robot carry N our joint has fewer modes than the individual agent models. We call our approach N min -sparse generalized shared control.

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Socially compliant mobile robot navigation via inverse reinforcement learning

Cited by 391 publications

References 49 publications

Planning and Decision-Making for Autonomous Vehicles

Planning and Decision-Making for Autonomous Vehicles

Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors

Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction

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