Human-in-the-loop robot teleoperation interfaces enable operators to control robots to complete complex tasks, as seen by the success of teams in the DARPA Robotics Challenge (DRC). In this work, we compare two human-in-the-loop planning interfaces, a 2D keyboard and mouse (KBM) interface modeled after those used in the DRC and a 3D virtual reality (VR) interface, for teleoperating a robot to perform navigation and manipulation tasks. In our study, we investigated operator performance, and cognitive workload while using the interface, as well as the perceived usability of each. We found that participants had better performance in both task types when using the KBM interface, however they experienced fewer collisions between the robot and the world in the VR interface. Given these findings, we recommend utilizing a KBM interface in low-risk situations where task performance is the primary factor. In high-risk scenarios, where collisions can be detrimental, we recommend using VR. With this work we aim to contribute to building effective and intuitive interfaces for human-in-the-loop planning to allow robots to complete complex tasks in challenging environments.

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

The FetchIt! Mobile Manipulation Challenge, held at the IEEE International Conference on Robots and Automation (ICRA) in May 2019, offered an environment with complex and integrated task sets, irregular objects, confined space, and machining, introducing new challenges in the mobile manipulation domain. Here we describe our efforts to address these challenges by demonstrating the assembly of a kit of mechanical parts in a caddy. In addition to implementation details, we examine the issues in this task set extensively, and we discuss our software architecture in the hope of providing a base for other researchers. To evaluate performance and consistency, we conducted 20 full runs, then examined failure cases with possible solutions. We conclude by identifying future research directions to address the open challenges.

It has been claimed that a main advantage of cognitive architectures (compared to other types of specialized robotic architectures) is that they are task-general and can thus learn to perform any task as long as they have the right perceptual and action primitives. In this paper, we provide empirical evidence for this claim by directly comparing a high-performing custom robotic architecture developed for the standardized robotic "FetchIt!" challenge task to a hybrid cognitive robotic architecture that allows for online one-shot task learning and task modifications through natural language instructions. The results show that there is no disadvantage of running the hybrid architecture (i.e., no significant difference in overall performance or computational overhead compared to the custom architecture) while adding the flexibility of online one-shot task instruction and modification not available in the custom architecture.

A significant amount of robotics research over the past decade has shown that many children with autism spectrum disorders (ASD) have a strong interest in robots and robot toys, concluding that robots are potential tools for the therapy of individuals with ASD. However, clinicians, who have the authority to approve robots in ASD therapy, are not convinced about the potential of robots. One major reason is that the research in this domain does not have a strong focus on the efficacy of robots. Robots in ASD therapy are end-user oriented technologies, the success of which depends on their demonstrated efficacy in real settings. This paper focuses on measuring the efficacy of robots in ASD therapy and, based on the data from a feasibility study, shows that the human-robot interaction (HRI) metrics commonly used in this research domain might not be sufficient.