Safe Explicable Robot Planning

Human expectations stem from their knowledge of the others and the world. Where human-robot interaction is concerned, such knowledge about the robot may be inconsistent with the ground truth, resulting in the robot not meeting its expectations. Explicable planning was previously introduced as a novel planning approach to reconciling human expectations and the optimal robot behavior for more interpretable robot decision-making. One critical issue that remains unaddressed is safety during explicable decision-making which can lead to explicable behaviors that are unsafe. We propose Safe Explicable Planning (SEP), which extends explicable planning to support the specification of a safety bound. The objective of SEP is to find a policy that generates a behavior close to human expectations while satisfying the safety constraints introduced by the bound, which is a special case of multi-objective optimization where the solution to SEP lies on the Pareto frontier. Under such a formulation, we propose a novel and efficient method that returns the safe explicable policy and an approximate solution. In addition, we provide theoretical proof for the optimality of the exact solution under the designer-specified bound. Our evaluation results confirm the applicability and efficacy of our method for safe explicable planning

Socially-Aware Navigation Planner Using Models of Human-Human Interaction

A real-time socially-aware navigation planner helps a mobile robot to navigate alongside humans in a socially acceptable manner. This navigation planner is a modification of nav_core package of Robot Operating System (ROS), based upon earlier work and further modified to use only egocentric sensors. The planner can be utilized to provide safe as well as socially appropriate robot navigation. Primitive features including interpersonal distance between the robot and an interaction partner and features of the environment (such as hallways detected in real-time) are used to reason about the current state of an interaction. Gaussian Mixture Models (GMM) are trained over these features from human-human interaction demonstrations of various interaction scenarios. This model is both used to discriminate different human actions related to their navigation behavior and to help in the trajectory selection process to provide a social-appropriateness score for a potential trajectory. This thesis presents a model based framework for navigation planning, a simulation-based evaluation of the model-based navigation behavior

A planning method for safe interaction between human arms and robot manipulators

This paper presents a planning method based on mapping moving obstacles into C-space for safe interaction between human arms and robot manipulators. In pre-processing phase, a hybrid distance metric is defined to select neighboring sampled nodes in C-space to construct a roadmap. Then, two kinds of mapping are constructed to determine invalid and dangerous edges in the roadmap for each basic cell decomposed in workspace. For updating the roadmap when an obstacle is moving, basic cells covering the obstacle's surfaces are mapped into the roadmap by using new positions of the surfaces points sampled on the obstacle. In query phase, in order to predict and avoid coming collisions and reach the goal efficiently, an interaction strategy with six kinds of planning actions of searching, updating, walking, waiting, dodging and pausing are designed. Simulated experiments show that the proposed method is efficient for safe interaction between two working robot manipulators and two randomly moving human arms.Computer Science, Artificial IntelligenceRoboticsCPCI-S(ISTP)

Trust-Based Control of Robotic Manipulators in Collaborative Assembly in Manufacturing

Human-robot interaction (HRI) is vastly addressed in the field of automation and manufacturing. Most of the HRI literature in manufacturing explored physical human-robot interaction (pHRI) and invested in finding means for ensuring safety and optimized effort sharing amongst a team of humans and robots. The recent emergence of safe, lightweight, and human-friendly robots has opened a new realm for human-robot collaboration (HRC) in collaborative manufacturing. For such robots with the new HRI functionalities to interact closely and effectively with a human coworker, new human-centered controllers that integrate both physical and social interaction are demanded. Social human-robot interaction (sHRI) has been demonstrated in robots with affective abilities in education, social services, health care, and entertainment. Nonetheless, sHRI should not be limited only to those areas. In particular, we focus on human trust in robot as a basis of social interaction. Human trust in robot and robot anthropomorphic features have high impacts on sHRI. Trust is one of the key factors in sHRI and a prerequisite for effective HRC. Trust characterizes the reliance and tendency of human in using robots. Factors within a robotic system (e.g. performance, reliability, or attribute), the task, and the surrounding environment can all impact the trust dynamically. Over-reliance or under-reliance might occur due to improper trust, which results in poor team collaboration, and hence higher task load and lower overall task performance. The goal of this dissertation is to develop intelligent control algorithms for the manipulator robots that integrate both physical and social HRI factors in the collaborative manufacturing. First, the evolution of human trust in a collaborative robot model is identified and verified through a series of human-in-the-loop experiments. This model serves as a computational trust model estimating an objective criterion for the evolution of human trust in robot rather than estimating an individual\u27s actual level of trust. Second, an HRI-based framework is developed for controlling the speed of a robot performing pick and place tasks. The impact of the consideration of the different level of interaction in the robot controller on the overall efficiency and HRI criteria such as human perceived workload and trust and robot usability is studied using a series of human-in-the-loop experiments. Third, an HRI-based framework is developed for planning and controlling the robot motion in performing hand-over tasks to the human. Again, series of human-in-the-loop experimental studies are conducted to evaluate the impact of implementation of the frameworks on overall efficiency and HRI criteria such as human workload and trust and robot usability. Finally, another framework is proposed for the cooperative manipulation of a common object by a team of a human and a robot. This framework proposes a trust-based role allocation strategy for adjusting the proactive behavior of the robot performing a cooperative manipulation task in HRC scenarios. For the mentioned frameworks, the results of the experiments show that integrating HRI in the robot controller leads to a lower human workload while it maintains a threshold level of human trust in robot and does not degrade robot usability and efficiency

Real-Time Robot Motion Planning Algorithms and Applications Under Uncertainty

Robot motion planning is an important problem for real-world robot applications. Recently, the separation of workspaces between humans and robots has been gradually fading, and there is strong interest in developing solutions where collaborative robots (cobots) can interact or work safely with humans in a shared space or in close proximity. When working with humans in real-world environments, the robots need to plan safe motions under uncertainty stemming from many sources such as noise of visual sensors, ambiguity of verbal instruction, and variety of human motions. In this thesis, we propose novel optimization-based and learning-based robot motion planning algorithms to deal with the uncertainties in real-world environments. To handle the input noise of visual cameras and the uncertainty of shape and pose estimation of surrounding objects, we present efficient probabilistic collision detection algorithms for Gaussian and non-Gaussian error distributions. By efficiently computing upper bounds of collision probability between an object and a robot, we present novel trajectory planning algorithms that guarantee that the collision probability at any trajectory point is less than a user-specified threshold. To enable human-robot interaction using natural language instructions, we present a mapping function from grounded linguistic semantics to the coefficients of the motion planning optimization problem. The mapping function considers task descriptions and motion-related constraints. For collaborative robots working with a human in close proximity, we present human intention and motion prediction algorithms for efficient task ordering and safe motion planning. The robot observes the human poses in real-time and predicts the future human motion based on the history of human poses. We also present an occlusion-aware robot motion planning algorithm that accounts for occlusion in the visual sensor data and uses learning-based techniques for trajectory planning. We highlight the benefits of our collision detection and robot motion planning algorithms with a 7-DOF Fetch robot arm in simulated and real-world environments.Doctor of Philosoph

Analysis and Synthesis of Effective Human-Robot Interaction at Varying Levels in Control Hierarchy

Robot controller design is usually hierarchical with both high-level task and motion planning and low-level control law design. In the presented works, we investigate methods for low-level and high-level control designs to guarantee joint performance of human-robot interaction (HRI). In the first work, a low-level method using the switched linear quadratic regulator (SLQR), an optimal control policy based on a quadratic cost function, is used. By incorporating measures of robot performance and human workload, it can be determined when to utilize the human operator in a method that improves overall task performance while reducing operator workload. This method is demonstrated via simulation using the complex dynamics of an autonomous underwater vehicle (AUV), showing this method can successfully overcome such scenarios while maintaining reduced workload. An extension of this work to path planning is also presented for the purposes of obstacle avoidance with simulation showing human planning successfully guiding the AUV around obstacles to reach its goals. In the high-level approach, formal methods are applied to a scenario where an operator oversees a group of mobile robots as they navigate an unknown environment. Autonomy in this scenario uses specifications written in linear temporal logic (LTL) to conduct symbolic motion planning in a guaranteed safe, though very conservative, approach. A human operator, using gathered environmental data, is able to produce a more efficient path. To aid in task decomposition and real-time switching, a dynamic human trust model is used. Simulations are given showing the successful implementation of this method