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Teleoperation enables controlling complex robot systems remotely, providing the ability to impart human expertise from a distance. However, these interfaces can be complicated to use as it is difficult to contextualize information about robot motion in the workspace from the limited camera feedback. Thus, it is required to study the best manner in which assistance can be provided to the operator that reduces interface complexity and effort required for teleoperation. Some techniques that provide assistance to the operator while freeform teleoperating include: (1) perception augmentation, like augmented reality visual cues and additional camera angles, increasing the information available to the operator; (2) action augmentation, like assistive autonomy and control augmentation, optimized to reduce the effort required by the operator while teleoperating. In this article, we investigate: (1) which aspects of dexterous telemanipulation require assistance; (2) the impact of perception and action augmentation in improving teleoperation performance; and (3) what factors impact the usage of assistance and how to tailor these interfaces based on the operators’ needs and characteristics. The findings from this user study and resulting post-study surveys will help identify task-based and user-preferred perception and augmentation features for teleoperation assistance. 

Teleoperation enables complex robot platforms to perform tasks beyond the scope of the current state-of-the-art robot autonomy by imparting human intelligence and critical thinking to these operations. For seamless control of robot platforms, it is essential to facilitate optimal situational awareness of the workspace for the operator through active telepresence cameras. However, the control of these active telepresence cameras adds an additional degree of complexity to the task of teleoperation. In this paper we present our results from the user study that investigates: (1) how the teleoperator learns or adapts to performing the tasks via active cameras modeled after camera placements on the TRINA humanoid robot; (2) the perception-action coupling operators implement to control active telepresence cameras, and (3) the camera preferences for performing the tasks. These findings from the human motion analysis and post-study survey will help us determine desired design features for robot teleoperation interfaces and assistive autonomy. 

This paper studies temporal planning in probabilistic environments, modeled as labeled Markov decision processes (MDPs), with user preferences over multiple temporal goals. Existing works reflect such preferences as a prioritized list of goals. This paper introduces a new specification language, termed prioritized qualitative choice linear temporal logic on finite traces, which augments linear temporal logic on finite traces with prioritized conjunction and ordered disjunction from prioritized qualitative choice logic. This language allows for succinctly specifying temporal objectives with corresponding preferences accomplishing each temporal task. The finite traces that describe the system's behaviors are ranked based on their dissatisfaction scores with respect to the formula. We propose a systematic translation from the new language to a weighted deterministic finite automaton. Utilizing this computational model, we formulate and solve a problem of computing an optimal policy that minimizes the expected score of dissatisfaction given user preferences. We demonstrate the efficacy and applicability of the logic and the algorithm on several case studies with detailed analyses for each. 

Robot teleoperation is an emerging field of study with wide applications in exploration, manufacturing, and healthcare, because it allows users to perform complex remote tasks while remaining distanced and safe. Haptic feedback offers an immersive user experience and expands the range of tasks that can be accomplished through teleoperation. In this paper, we present a novel wearable haptic feedback device for a teleoperation system that applies kinesthetic force feedback to the fingers of a user. The proposed device, called a ‘haptic muscle’, is a soft pneumatic actuator constructed from a fabric-silicone composite in a toroidal structure. We explore the requirements of the ideal haptic feedback mechanism, construct several haptic muscles using different materials, and experimentally determine their dynamic pressure response as well as sensitivity (their ability to communicate small changes in haptic feedback). Finally, we integrate the haptic muscles into a data glove and a teleoperation system and perform several user tests. Our results show that most users could detect detect force changes as low as 3% of the working range of the haptic muscles. We also find that the haptic feedback causes users to apply up to 52% less force on an object while handling soft and fragile objects with a teleoperation system. 

In this paper, we study planning in stochastic systems, modeled as Markov decision processes (MDPs), with preferences over temporally extended goals. Prior work on temporal planning with preferences assumes that the user preferences form a total order, meaning that every pair of outcomes are comparable with each other. In this work, we consider the case where the preferences over possible outcomes are a partial order rather than a total order. We first introduce a variant of deterministic finite automaton, referred to as a preference DFA, for specifying the user's preferences over temporally extended goals. Based on the order theory, we translate the preference DFA to a preference relation over policies for probabilistic planning in a labeled MDP. In this treatment, a most preferred policy induces a weak-stochastic nondominated probability distribution over the finite paths in the MDP. The proposed planning algorithm hinges on the construction of a multi-objective MDP. We prove that a weak-stochastic nondominated policy given the preference specification is Pareto-optimal in the constructed multi-objective MDP, and vice versa. Throughout the paper, we employ a running example to demonstrate the proposed preference specification and solution approaches. We show the efficacy of our algorithm using the example with detailed analysis, and then discuss possible future directions. 

Motion tracking interfaces are intuitive for free-form teleoperation tasks. However, efficient manipulation control can be difficult with such interfaces because of issues like the interference of unintended motions and the limited precision of human motion control. The limitation in control efficiency reduces the operator's performance and increases their workload and frustration during robot teleoperation. To improve the efficiency, we proposed separating controlled degrees of freedom (DoFs) and adjusting the motion scaling ratio of a motion tracking interface. The motion tracking of handheld controllers from a Virtual Reality system was used for the interface. We separated the translation and rotational control into: 1) two controllers held in the dominant and non-dominant hands and 2) hand pose tracking and trackpad inputs of a controller. We scaled the control mapping ratio based on 1) the environmental constraints and 2) the teleoperator's control speed. We further conducted a user study to investigate the effectiveness of the proposed methods in increasing efficiency. Our results show that the separation of position and orientation control into two controllers and the environment-based scaling methods perform better than their alternatives.