Frontiers in Robotics and AI最新文献

The concept of anthropomorphism is crucial in enhancing interactions between service robots and humans, serving as a key consideration in the design of these robots. Nevertheless, the specific mechanisms by which the anthropomorphic traits of service robots influence customer behavioral responses remain inadequately understood. Furthermore, the incorporation of anthropomorphic robotic technology into customer service operational strategies presents a significant challenge for businesses. To explore the underlying mechanisms through which the anthropomorphic characteristics of service robots impact customer acceptance, this study conducted a series of six experiments to empirically test the proposed hypotheses. The empirical findings indicate notable differences in customer switching behaviors and selection quantity metrics, which can be linked to service contexts characterized by varying degrees of behavioral anthropomorphism. Additionally, social presence has been identified as a mediating variable that affects the relationship between the anthropomorphism of service robot behavior and its influence on customer variety-seeking behavior. The situational context of customer decision-making is also found to moderate the relationship between social presence and variety-seeking behavior. Consequently, it is recommended that service organizations implement service robots with diverse anthropomorphic features to enhance customer acquisition, cultivate loyalty, and improve overall marketing effectiveness.

This paper presents methods for finding optimal configurations and actuator forces/torques to maximise contact wrenches in a desired direction for underwater vehicle manipulator systems (UVMS). The wrench maximisation problem is formulated as a bi-level optimisation problem, with upper-level variables in a low-dimensional parameterised redundancy space, and a linear lower-level problem. We additionally consider the cases of one or more manipulators with multiple contact forces, maximising the wrench capability while tracking a trajectory and generating large wrench impulses using dynamic motions. The specific cases of maximising force to lift a heavy load and maximising torque during a valve-turning operation are considered. Extensive experimental results are presented using a 6 degree of freedom (DOF) underwater robotic platform equipped with a 4DOF manipulator and show significant increases in the wrench capability compared to existing methods for mobile manipulators.

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific computational building blocks that prevent code duplication and enable robots to adapt their software themselves. The envisaged algorithms are numerical solvers based on graph structures. In this article, we focus on kinematics and dynamics algorithms, but examples such as message passing on probabilistic networks and factor graphs or cascade control diagrams fall under the same pattern. The tools rely on mature standards from the Semantic Web. They first synthesize algorithms symbolically, from which they then generate efficient code. The use case is an overactuated mobile robot with two redundant arms.

This article presents the development of a socially interactive industrial robot. An Avatar is used to embody a cobot for collaborative industrial assembly tasks. The embodied covatar (cobot plus its avatar) is introduced to support Flow experiences through co-regulation, interactive emotion regulation guidance. A real-time continuous emotional modeling method and an aligned transparent behavioral model, BASSF (Boredom, Anxiety, Self-efficacy, Self-compassion, Flow) is developed. The BASSF model anticipates and co-regulates counterproductive emotional experiences of operators working under stress with cobots on tedious industrial tasks. The targeted Flow experience is represented in the three-dimensional Pleasure, Arousal, and Dominance (PAD) space. We present how, despite their noisy nature, PAD signals can be used to drive the BASSF model with its theory-based interventions. The empirical results and analysis provides empirical support for the theoretically defined model, and clearly points to the need for data pre-filtering and per-user calibration. The proposed post-processing method helps quantify the parameters needed to control the frequency of intervention of the agent; still leaving the experimenter with a run-time adjustable global control of its sensitivity. A controlled empirical study (Study 1, N = 20), tested the model's main theoretical assumptions about Flow, Dominance, Self-Efficacy, and boredom, to legitimate its implementation in this context. Participants worked on a task for an hour, assembling pieces in collaboration with the covatar. After the task, participants completed questionnaires on Flow, their affective experience, and Self-Efficacy, and they were interviewed to understand their emotions and regulation during the task. The results from Study 1 suggest that the Dominance dimension plays a vital role in task-related settings as it predicts the participants' Self-Efficacy and Flow. However, the relationship between Flow, pleasure, and arousal requires further investigation. Qualitative interview analysis revealed that participants regulated negative emotions, like boredom, also without support, but some strategies could negatively impact wellbeing and productivity, which aligns with theory. Additional results from a first evaluation of the overall system (Study 2, N = 12) align with these findings and provide support for the use of socially interactive industrial robots to support wellbeing, job satisfaction, and involvement, while reducing unproductive emotional experiences and their regulation.

Introduction: This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, contributing to the Industry 4.0 paradigm. Industry 4.0, emphasizing data-driven processes, connectivity, and robotics, enhances accessibility and sustainability across the value chain. Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.

Methods: The proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. Tracking algorithms and depth-sensing technologies are integrated to enable 3D spatial awareness during pick-and-place operations. The system incorporates vacuum gripping technology with compliance control for optimal grasping and manipulation points, using a Franka Emika robot arm.

Results: The system successfully demonstrates its capability to automate the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader. Rigorous lab testing showed high accuracy in bag detection and manipulation under varying environmental conditions, as well as reliable performance in handling and processing tasks. The approach effectively addressed challenges related to transparency, plastic bag manipulation and industrial automation.

Discussion: The results indicate that the proposed solution is highly effective for industrial applications requiring precision and adaptability, aligning with the principles of Industry 4.0. By combining advanced vision algorithms, depth sensing, and compliance control, the system offers a robust method for automating challenging tasks. The integration of cobots into such workflows demonstrates significant potential for enhancing efficiency, safety, and sustainability in industrial settings.

The usage of broad sets of training data is paramount to evolve adaptive agents. In this respect, competitive co-evolution is a widespread technique in which the coexistence of different learning agents fosters adaptation, which in turn makes agents experience continuously varying environmental conditions. However, a major pitfall is related to the emergence of endless limit cycles where agents discover, forget and rediscover similar strategies during evolution. In this work, we investigate the use of competitive co-evolution for synthesizing progressively better solutions. Specifically, we introduce a set of methods to measure historical and global progress. We discuss the factors that facilitate genuine progress. Finally, we compare the efficacy of four qualitatively different algorithms, including two newly introduced methods. The selected algorithms promote genuine progress by creating an archive of opponents used to evaluate evolving individuals, generating archives that include high-performing and well-differentiated opponents, identifying and discarding variations that lead to local progress only (i.e., progress against the opponents experienced and retrogressing against others). The results obtained in a predator-prey scenario, commonly used to study competitive evolution, demonstrate that all the considered methods lead to global progress in the long term. However, the rate of progress and the ratio of progress versus retrogressions vary significantly among algorithms. In particular, our outcomes indicate that the Generalist method introduced in this work outperforms the other three considered methods and represents the only algorithm capable of producing global progress during evolution.

Introduction: Inspired by natural phenomena, generations of researchers have been investigating how a swarm of robots can act coherently and purposefully, when individual robots can only sense and communicate with nearby peers, with no means of global communications and coordination. In this paper, we will show that swarms can perform better, when they self-adapt to admit heterogeneous behavior roles.

Methods: We model a foraging swarm task as an extensive-form fully-cooperative game, in which the swarm reward is an additive function of individual contributions (the sum of collected items). To maximize the swarm reward, previous work proposed using distributed reinforcement learning, where each robot adapts its own collision-avoidance decisions based on the Effectiveness Index reward (EI). EI uses information about the time between their own collisions (information readily available even to simple physical robots). While promising, the use of EI is brittle (as we show), since robots that selfishly seek to optimize their own EI (minimizing time spent on collisions) can actually cause swarm-wide performance to degrade.

Results: To address this, we derive a reward function from a game-theoretic view of swarm foraging as a fully-cooperative, unknown horizon repeating game. We demonstrate analytically that the total coordination overhead of the swarm (total time spent on collision-avoidance, rather than foraging per-se) is directly tied to the total utility of the swarm: less overhead, more items collected. Treating every collision as a stage in the repeating game, the overhead is bounded by the total EI of all robots. We then use a marginal-contribution (difference-reward) formulation to derive individual rewards from the total EI. The resulting Aligned Effective Index $\left(AEI\right)$ reward has the property that each individual can estimate the impact of its decisions on the swarm: individual improvements translate to swarm improvements. We show that $AEI$ provably generalizes previous work, adding a component that computes the effect of counterfactual robot absence. Different assumptions on this counterfactual lead to bounds on $AEI$ from above and below.

Discussion: While the theoretical analysis clarifies both assumptions and gaps with respect to the reality of robots, experiments with real and simulated robots empirically demonstrate the efficacy of the approach in practice, and the importance of behavioral (decision-making) diversity in optimizing swarm goals.

Biological vision systems simultaneously learn to efficiently encode their visual inputs and to control the movements of their eyes based on the visual input they sample. This autonomous joint learning of visual representations and actions has previously been modeled in the Active Efficient Coding (AEC) framework and implemented using traditional frame-based cameras. However, modern event-based cameras are inspired by the retina and offer advantages in terms of acquisition rate, dynamic range, and power consumption. Here, we propose a first AEC system that is fully implemented as a Spiking Neural Network (SNN) driven by inputs from an event-based camera. This input is efficiently encoded by a two-layer SNN, which in turn feeds into a spiking reinforcement learner that learns motor commands to maximize an intrinsic reward signal. This reward signal is computed directly from the activity levels of the first two layers. We test our approach on two different behaviors: visual tracking of a translating target and stabilizing the orientation of a rotating target. To the best of our knowledge, our work represents the first ever fully spiking AEC model.