This study evaluates the performance and usability of Mixed Reality (MR), Virtual Reality (VR), and camera stream interfaces for remote error resolution tasks, such as correcting warehouse packaging errors. Specifically, we consider a scenario where a robotic arm halts after detecting an error, requiring a remote operator to intervene and resolve it via pick-and-place actions. Twenty-one participants performed simulated pick-and - place tasks using each interface. A linear mixed model (LMM) analysis of task resolution time, usability scores (SUS), and mental workload scores (NASA- TLX) showed that the MR interface outperformed both VR and camera interfaces. MR enabled significantly faster task completion, was rated higher in usability, and was perceived to be less cognitively demanding. Notably, the MR interface, which projected a virtual robot onto a physical table, provided superior spatial understanding and physical reference cues. Post-study surveys further confirmed participants' preference for MR over other interfaces.

We propose a novel hand-object contact detection system based on grasp quality metrics extracted from object and hand poses, and evaluated its performance using the DexYCB dataset. Our evaluation demonstrated the system's high accuracy (approaching 90%). Future work will focus on a real-time implementation using vision-based estimation, and integrating it to a robot-to-human handover system.

This study investigates the subjective experiences of users in two robotic object delivery methods: direct handover and table placement, when users are occupied with another task. A user study involving 15 participants engaged in a typing game revealed that table placement significantly enhances user experience compared to direct handovers, particularly in terms of satisfaction, perceived safety and intuitiveness. Additionally, handovers negatively impacted typing performance, while all participants expressed a clear preference for table placement as the delivery method. These findings highlight the advantages of table placement in scenarios requiring minimal user disruption.

Online evaluation is increasingly adopted in robotics research, providing an efficient approach to collect data from large and diverse populations. However, there have been ongoing debates about online studies as a proxy for in-person studies, especially where a participant passively observes video of robot behaviours or interaction. We conduct an online video comparison study (N=178) evaluating three robot handover policies in a collaborative assembly task, namely an adaptive autonomous policy, a non-adaptive scripted policy, and teleoperation. Participants watched three sets of videos in third-person view, each consisting of 9 sequential handovers executing one of the policies. Compared to in-person participants in two previous studies who evaluated handovers as users, online participants were observant of different robot behaviours and human-robot collaboration contexts, with 76.4% and 71.9% recognising the adaptive handovers exhibited by the teleoperated and autonomous robot, respectively. However, as observers, online participants showed more critical subjective perceptions compared to the in-person participants with a user’s perspective. They valued efficiency over adaptation with twice more autonomous handovers rated as being too late compared to scripted handovers. Our work highlights the need to consider user contexts when evaluating human-robot collaboration.

Reliable detection of conversational errors and user-initiated corrections is critical for effective human-robot interaction (HRI). In this study, we present a comprehensive multimodal approach leveraging temporal window processing, targeted feature engineering, and a MiniRocket + Ridge classification pipeline to address the challenges introduced by the ERR@HRI 2.0 dataset. Our methodology systematically integrates multimodal data streams, including facial expressions, acoustic features, and linguistic embeddings, to predict robot failures and user reactions. Experimental results demonstrate significant improvements over baseline models in event-level detection performance. Notably, linguistic features derived from transcript embeddings emerged as the most informative modality, substantially enhancing model performance. However, we observed challenges associated with managing false positives at the event level, suggesting avenues for future refinement in adaptive thresholding and sequential post-processing techniques. Our findings underscore the importance of careful feature selection and robust temporal modelling in developing effective real-time error detection systems for conversational robots. Our code is available online. https://github.com/Ruddy202/err-hri-2.0-armas.git.

We present a multimodal traffic light state detection using vision and sound, from the viewpoint of a quadruped robot navigating in urban settings. This is a challenging problem because of the visual occlusions and noise from robot locomotion. Our method combines features from raw audio with the ratios of red and green pixels within bounding boxes, identified by established vision-based detectors. The fusion method aggregates features across multiple frames in a given timeframe, increasing robustness and adaptability. Results show that our approach effectively addresses the challenge of visual occlusion and surpasses the performance of single-modality solutions when the robot is in motion. This study serves as a proof of concept, highlighting the significant, yet often overlooked, potential of multi-modal perception in robotics.

Service robots are increasingly employed in the hospitality industry for delivering food orders in restaurants. However, in current practice the robot often arrives at a fixed location for each table when delivering orders to different patrons in the same dining group, thus requiring a human staff member or the customers themselves to identify and retrieve each order. This study investigates how to improve the robot’s service behaviours to facilitate clear intention communication to a group of users, thus achieving accurate delivery and positive user experiences. Specifically, we conduct user studies (N=30) with a Temi service robot as a representative delivery robot currently adopted in restaurants. We investigated two factors in the robot’s intent communication, namely visualisation and movement trajectories, and their influence on the objective and subjective interaction outcomes. A robot personalising its movement trajectory and stopping location in addition to displaying a visualisation of the order yields more accurate intent communication and successful order delivery, as well as more positive user perception towards the robot and its service. Our results also showed that individuals in a group have different interaction experiences.

We study human-robot handovers in a naturalistic collaboration scenario, where a mobile manipulator robot assists a person during a crafting session by providing and retrieving objects used for wooden piece assembly (functional activities) and painting (creative activities). We collect quantitative and qualitative data from 20 participants in a Wizard-of-Oz study, generating the Functional And Creative Tasks Human-Robot Collaboration dataset (the FACT HRC dataset), available to the research community. This work illustrates how social cues and task context inform the temporal-spatial coordination in human-robot handovers, and how human-robot collaboration is shaped by and in turn influences people's functional and creative activities.

Navigating effectively and safely in unknown urban environments is a crucial ability for service robot applications such as last-mile package delivery. To reach the entrance of its target destination, the robot must make informed local and global path planning decisions. We present a mapless global planning strategy based on pedestrian following. Our method allows the robot to exploit natural routes taken by surrounding pedestrians to make informed and efficient path planning decisions for reaching its goal. The algorithm also includes a recovery system to assist the robot when insufficient progress is made (i.e. robot stuck in dead end). Once the robot is within the vicinity of the target building, a wall following behaviour is used to reach the entrance of the target building. Simulated experiments and a proof-of-concept demonstration on a real robot were shown to validate the approach.

We propose a robotic manipulation method that can pivot objects on a surface using vision, wrist force and tactile sensing. We aim to control the rotation of an object around the grip point of a parallel gripper by allowing rotational slip, while maintaining a desired wrist force profile. Our approach runs an end-effector position controller and a gripper width controller concurrently in a closed loop. The position controller maintains a desired force using vision and wrist force. The gripper controller uses tactile sensing to keep the grip firm enough to prevent translational slip, but loose enough to allow rotational slip. Our sensor-based control approach relies on matching a desired force profile derived from object dimensions and weight, as well as vision-based monitoring of the object pose. The gripper controller uses tactile sensors to detect and prevent translational slip by tightening the grip when needed. Experimental results where the robot was tasked with rotating cuboid objects 90 degrees show that the multi-modal pivoting approach was able to rotate the objects without causing lift or translational slip, and was more energy-efficient compared to using a single sensor modality or pick-and-place.

Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all six degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches In addition, we found two “supporting methods” around grasping that use deep learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research.

Existing approaches to direct robot-to-human handovers are typically implemented on fixed-base robot arms, or on mobile manipulators that come to a full stop before performing the handover. We propose "on-the-go" handovers which permit a moving mobile manipulator to hand over an object to a human without stopping. The on-the-go handover motion is generated with a reactive controller that allows simultaneous control of the base and the arm. In a user study, human receivers subjectively assessed on-the-go handovers to be more efficient, predictable, natural, better timed and safer than handovers that implemented a "stop-and-deliver" behavior.

Safety is a basic requirement in any human-robot collaboration scenario. To ensure user safety, from both physical and psychological aspects, we propose a novel Virtual Barrier system facilitated by an augmented reality interface1. Our system provides two types of Virtual Barriers to ensure safety: 1) a Virtual Person Barrier which encapsulates and follows the user to protect them from collisions with the robot, and 2) Virtual Obstacle Barriers which users can spawn to protect objects or regions that the robot should not enter. Our system utilizes augmented reality to visually display these protective barriers to the user during operation. To enable effective human-robot collaboration, our system automatically replans the robot’s motion when potential collisions are detected as a result of a barrier intersecting the robot’s planned path. Comparing our novel system with a standard 2D display interface in a user study with a mock industrial manufacturing task showed that our system increases both physical and psychological safety, task efficiency and interaction intuitiveness.

Humans are highly skilled in communicating their intent for when and where a handover would occur. However, even the state-of-the-art robotic implementations for handovers typically lack of such communication skills. This study investigates visualization of the robot’s internal state and intent for Human-to-Robot Handovers using Augmented Reality. Specifically, we explore the use of visualized 3D models of the object and the robotic gripper to communicate the robot’s estimation of where the object is and the pose in which the robot intends to grasp the object. We tested this design via a user study with 16 participants, in which each participant handed over a cube-shaped object to the robot 12 times. Results show communicating robot intent via augmented reality substantially improves the perceived experience of the users for handovers. Results also indicate that the effectiveness of augmented reality is even more pronounced for the perceived safety and fluency of the interaction when the robot makes errors in localizing the object.

The DARPA subterranean challenge requires teams of robots to traverse difficult and diverse underground environments. Traversing small gaps is one of the challenging scenarios that robots encounter. Imperfect sensor information makes it difficult for classical navigation methods, where behaviours require significant manual fine tuning. In this paper we present a deep reinforcement learning method for autonomously navigating through small gaps, where contact between the robot and the gap may be required. We first learn a gap behaviour policy to get through small gaps (only centimeters wider than the robot). We then learn a goal-conditioned behaviour selection policy that determines when to activate the gap behaviour policy. We train our policies in simulation and demonstrate their effectiveness with a large tracked robot in simulation and on the real platform. In simulation experiments, our approach achieves 93% success rate when the gap behaviour is activated manually by an operator, and 63% with autonomous activation using the behaviour selection policy. In real robot experiments, our approach achieves a success rate of 73% with manual activation, and 40% with autonomous behaviour selection. While we show the feasibility of our approach in simulation, the difference in performance between simulated and real world scenarios highlight the difficulty of direct sim-to-real transfer for deep reinforcement learning policies. In both the simulated and real world environments alternative methods were unable to traverse the gap.

Legged robots often use separate control policies that are highly engineered for traversing difficult terrain such as stairs, gaps, and steps, where switching between policies is only possible when the robot is in a region that is common to adjacent controllers. Deep Reinforcement Learning (DRL) is a promising alternative to hand-crafted control design, though typically requires the full set of test conditions to be known before training. DRL policies can result in complex (often unrealistic) behaviours that have few or no overlapping regions between adjacent policies, making it difficult to switch behaviours. In this work we develop multiple DRL policies with Curriculum Learning (CL), each that can traverse a single respective terrain condition, while ensuring an overlap between policies. We then train a network for each destination policy that estimates the likelihood of successfully switching from any other policy. We evaluate our switching method on a previously unseen combination of terrain artifacts and show that it performs better than heuristic methods. While our method is trained on individual terrain types, it performs comparably to a Deep Q Network trained on the full set of terrain conditions. This approach allows the development of separate policies in constrained conditions with embedded prior knowledge about each behaviour, that is scalable to any number of behaviours, and prepares DRL methods for applications in the real world.

We present an approach for visualizing mobile robots through an Augmented Reality headset when there is no line-of-sight visibility between the robot and the human. Three elements are visualized in Augmented Reality: 1) Robot’s 3D model to indicate its position, 2) An arrow emanating from the robot to indicate its planned movement direction, and 3) A 2D grid to represent the ground plane. We conduct a user study with 18 participants, in which each participant are asked to retrieve objects, one at a time, from stations at the two sides of a T-junction at the end of a hallway where a mobile robot is roaming. The results show that visualizations improved the perceived safety and efficiency of the task and led to participants being more comfortable with the robot within their personal spaces. Furthermore, visualizing the motion intent in addition to the robot model was found to be more effective than visualizing the robot model alone. The proposed system can improve the safety of automated warehouses by increasing the visibility and predictability of robots.

An appropriate user interface to collect human demonstration data for deformable object manipulation has been mostly overlooked in the literature. We present an inter-action design for demonstrating cloth folding to robots. Users choose pick and place points on the cloth and can preview a visualization of a simulated cloth before real-robot execution. Two interfaces are proposed: A 2D display-and-mouse interface where points are placed by clicking on an image of the cloth, and a 3D Augmented Reality interface where the chosen points are placed by hand gestures. We conduct a user study with 18 participants, in which each user completed two sequential folds to achieve a cloth goal shape. Results show that while both interfaces were acceptable, the 3D interface was more suitable for understanding the task, and the 2D interface was suitable for repetition. Results also found that fold previews improve three key metrics: task efficiency, the ability to predict the final shape of the cloth, and overall user satisfaction.

This article surveys the literature on human–robot object handovers. A handover is a collaborative joint action, where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: a prehandover phase and the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, and learning) and the physical level (e.g., motion, grasping, and grip release) of the handover, we also discuss safety. We compare the behaviors displayed during human-to-human handovers to the state of the art of robotic assistants and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches.

The increasing presence of robots alongside humans, such as in human-robot teams in manufacturing, gives rise to research questions about the kind of behaviors people prefer in their robot counterparts. We term actions that support interaction by reducing future interference with others as supportive robot actions and investigate their utility in a co-located manipulation scenario. We compare two robot modes in a shared table pick-and-place task: (1) Task-oriented: the robot only takes actions to further its task objective and (2) Supportive: the robot sometimes prefers supportive actions to task-oriented ones when they reduce future goal-conflicts. Our experiments in simulation, using a simplified human model, reveal that supportive actions reduce the interference between agents, especially in more difficult tasks, but also cause the robot to take longer to complete the task. We implemented these modes on a physical robot in a user study where a human and a robot perform object placement on a shared table. Our results show that a supportive robot was perceived more favorably as a coworker and also reduced interference with the human in one of two scenarios. However, it also took longer to complete the task highlighting an interesting trade-off between task-efficiency and human-preference that needs to be considered before designing robot behavior for close-proximity manipulation scenarios.

We present a robotic system capable of navigating autonomously by following a line and taking good quality pictures of people. When a group of people is detected, the robot rotates towards them and then back to line while continuously taking pictures from different angles. Each picture is processed in the cloud where its quality is estimated in a two-stage algorithm. First, features such as the face orientation and likelihood of facial emotions are input to a fully connected neural network to assign a quality score to each face. Second, a representation is extracted by abstracting faces from the image and it is input to a Convolutional Neural Network (CNN) to classify the quality of the overall picture. We collected a dataset in which a picture was labeled as good quality if subjects are well-positioned in the image and oriented towards the camera with a pleasant expression. Our approach detected the quality of pictures with 78.4% accuracy in this dataset and received a better mean user rating (3.71/5) than a heuristic method that uses photographic composition procedures in a study where 97 human judges rated each picture. Statistical analysis against the state-of-the-art verified the quality of the resulting pictures.

Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks, however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To mitigate forgetting, we propose an experience replay process that augments the standard FIFO buffer and selectively stores experiences in a long-term memory. We explore four strategies for selecting which experiences will be stored: favoring surprise, favoring reward, matching the global training distribution, and maximizing coverage of the state space. We show that distribution matching successfully prevents catastrophic forgetting, and is consistently the best approach on all domains tested. While distribution matching has better and more consistent performance, we identify one case in which coverage maximization is beneficial---when tasks that receive less trained are more important. Overall, our results show that selective experience replay, when suitable selection algorithms are employed, can prevent catastrophic forgetting.

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.

Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an Autonomous Vehicle (AV) drive together. We propose a planning framework where the degree to which the AV considers the other agent's reward is controlled by a selfishness factor. We test our approach on a simulated two-lane highway where the AV and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen. Our results on double lane merging suggest it to be a non-zero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic.

Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on several relevant metrics that measure both safety and efficiency.

Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale rollout on public roads.

Robot path planning in human environments benefits significantly from considering more than obstacle avoidance, and recent works in this area proposed safety and comfort considerations. One shortcoming of current approaches is that humans' behavior is modeled as independent of robot's motions. In this work, we aim to give this anticipation ability to a robot by simulating people's reaction to robot's motion during planning. Our approach is based on extracting a static plan using A* search on the grid map by minimizing safety, disturbance and path length costs and then refining it by simulating humans' reaction using the Social Force Model. With two example scenarios in simulation and two on the real system, we provide qualitative examination of the resulting robot paths and demonstrate that robots can exhibit social behaviors that is not possible to model with standard approaches. This work serves as a primer for quantitative user studies, and we hope will urge future robot path planners to consider a richer set of social capabilities.

Guidance for human navigation using a vibro-tactile belt interface and robot-like motion planning

We present a method for a mobile robot to follow a person autonomously where there is an interaction between the robot and human during following. The planner takes into account the predicted trajectory of the human and searches future trajectories of the robot for the path with the highest utility. Contrary to traditional motion planning, instead of determining goal points close to the person, we introduce a task dependent goal function which provides a map of desirable areas for the robot to be at, with respect to the person. The planning framework is flexible and allows encoding of different social situations with the help of the goal function. We implemented our approach on a telepresence robot and conducted a controlled user study to evaluate the experiences of the users on the remote end of the telepresence robot. The user study compares manual teleoperation to our autonomous method for following a person while having a conversation. By designing a behavior specific to a flat screen telepresence robot, we show that the person following behavior is perceived as safe and socially acceptable by remote users. All 10 participants preferred our autonomous following method over manual teleoperation.

We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation.