Human-centered collaborative robots with deep reinforcement learning

Ghadirzadeh, Ali; Chen, Xi; Yin, Wang; Yi, Zhou; Björkman, Mårten; Kragić, Danica

doi:10.48550/arxiv.2007.01009

Cited by 1 publication

(1 citation statement)

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“…They are gaining popularity in robotics because they are highly flexible, reusable, and well suited to define deliberative elements in the model-based design of control architectures. The use of BTs in robotics [1] spans from manipulation [2]- [4] to task planning [5], [6], human-robot interaction [7]- [9] to learning [10], [11], UAVs [12], [13], and system analysis [14]- [16]. Moreover, BTs are used in the Boston Dynamics's Spot SDKs to model the robot's mission [17] and in the Navigation Stack of ROS2 [18].…”

Section: Introductionmentioning

confidence: 99%

Formalizing the Execution Context of Behavior Trees for Runtime Verification of Deliberative Policies

Colledanchise,

Cicala,

Domenichelli

et al. 2021

Preprint

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Our research aims to enable automated property verification of deliberative components in robot control architectures. We focus on a formalization of the execution context of Behavior Trees (BTs) to provide a scalable, yet formally grounded, methodology to enable runtime verification and prevent unexpected robot behaviors to hamper deployment. To this end, we consider a message-passing model that accommodates both synchronous and asynchronous composition of parallel components, in which BTs and other components execute and interact according to the communication patterns commonly adopted in robotic software architectures. We introduce a formal property specification language to encode requirements and build runtime monitors. We performed a set of experiments both on simulations and on the real robot, demonstrating the feasibility of our approach in a realistic application, and its integration in a typical robot software architecture. We also provide an OS-level virtualization environment to reproduce the experiments in the simulated scenario.

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