Puppeteer Your Robot: Augmented Reality Leader-Follower Teleoperation

High-quality demonstrations are necessary when learning complex and challenging manipulation tasks. In this work, we introduce an approach to puppeteer a robot by controlling a virtual robot in an augmented reality setting. Our system allows for retaining the advantages of being intuitive from a physical leader-follower side while avoiding the unnecessary use of expensive physical setup. In addition, the user is endowed with additional information using augmented reality. We validate our system with a pilot study n=10 on a block stacking and rice scooping tasks where the majority rates the system favorably. Oculus App and corresponding ROS code are available on the project website: https://ar-puppeteer.github.io/

Unfolding the Literature: A Review of Robotic Cloth Manipulation

The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on rigid objects cannot fully address. The increasing interest within the community in textile perception and manipulation has led to new methods that aim to address challenges in modeling, perception, and control, resulting in significant progress. However, this progress is often tailored to one specific textile or a subcategory of these textiles. To understand what restricts these methods and hinders current approaches from generalizing to a broader range of real-world textiles, this review provides an overview of the field, focusing specifically on how and to what extent textile variations are addressed in modeling, perception, benchmarking, and manipulation of textiles. We finally conclude by identifying key open problems and outlining grand challenges that will drive future advancements in the field.

Vision Beyond Boundaries: An Initial Design Space of Domain-specific Large Vision Models in Human-robot Interaction

The emergence of Large Vision Models (LVMs) is following in the footsteps of the recent prosperity of Large Language Models (LLMs) in following years. However, there's a noticeable gap in structured research applying LVMs to Human-Robot Interaction (HRI), despite extensive evidence supporting the efficacy of vision models in enhancing interactions between humans and robots. Recognizing the vast and anticipated potential, we introduce an initial design space that incorporates domain-specific LVMs, chosen for their superior performance over normal models. We delve into three primary dimensions: HRI contexts, vision-based tasks, and specific domains. The empirical validation was implemented among 15 experts across six evaluated metrics, showcasing the primary efficacy in relevant decision-making scenarios. We explore the process of ideation and potential application scenarios, envisioning this design space as a foundational guideline for future HRI system design, emphasizing accurate domain alignment and model selection.

Hyperbolic Delaunay Geometric Alignment

Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) -- a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets. Furthermore, we showcase the potential of HyperDGA for evaluating latent representations inferred by a Hyperbolic Variational Auto-Encoder.

Will You Participate? Exploring the Potential of Robotics Competitions on Human-centric Topics

This paper presents findings from an exploratory needfinding study investigating the research current status and potential participation of the competitions on the robotics community towards four human-centric topics: safety, privacy, explainability, and federated learning. We conducted a survey with 34 participants across three distinguished European robotics consortia, nearly 60% of whom possessed over five years of research experience in robotics. Our qualitative and quantitative analysis revealed that current mainstream robotic researchers prioritize safety and explainability, expressing a greater willingness to invest in further research in these areas. Conversely, our results indicate that privacy and federated learning garner less attention and are perceived to have lower potential. Additionally, the study suggests a lack of enthusiasm within the robotics community for participating in competitions related to these topics. Based on these findings, we recommend targeting other communities, such as the machine learning community, for future competitions related to these four human-centric topics.

Low-Cost Teleoperation with Haptic Feedback through Vision-based Tactile Sensors for Rigid and Soft Object Manipulation

Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense of touch and expand their capability drastically. However, many tasks still need human intervention/guidance. For this reason, we present a teleoperation framework designed to provide haptic feedback to human operators based on the data from camera-based tactile sensors mounted on the robot gripper. Partial autonomy is introduced to prevent slippage of grasped objects during task execution. Notably, we rely exclusively on low-cost off-the-shelf hardware to realize an affordable solution. We demonstrate the versatility of the framework on nine different objects ranging from rigid to soft and fragile ones, using three different operators on real hardware.

Visual Action Planning with Multiple Heterogeneous Agents

Visual planning methods are promising to handle complex settings where extracting the system state is challenging. However, none of the existing works tackles the case of multiple heterogeneous agents which are characterized by different capabilities and/or embodiment. In this work, we propose a method to realize visual action planning in multi-agent settings by exploiting a roadmap built in a low-dimensional structured latent space and used for planning. To enable multi-agent settings, we infer possible parallel actions from a dataset composed of tuples associated with individual actions. Next, we evaluate feasibility and cost of them based on the capabilities of the multi-agent system and endow the roadmap with this information, building a capability latent space roadmap (C-LSR). Additionally, a capability suggestion strategy is designed to inform the human operator about possible missing capabilities when no paths are found. The approach is validated in a simulated burger cooking task and a real-world box packing task.

A Robotic Skill Learning System Built Upon Diffusion Policies and Foundation Models

In this paper, we build upon two major recent developments in the field, Diffusion Policies for visuomotor manipulation and large pre-trained multimodal foundational models to obtain a robotic skill learning system. The system can obtain new skills via the behavioral cloning approach of visuomotor diffusion policies given teleoperated demonstrations. Foundational models are being used to perform skill selection given the user's prompt in natural language. Before executing a skill the foundational model performs a precondition check given an observation of the workspace. We compare the performance of different foundational models to this end as well as give a detailed experimental evaluation of the skills taught by the user in simulation and the real world. Finally, we showcase the combined system on a challenging food serving scenario in the real world. Videos of all experimental executions, as well as the process of teaching new skills in simulation and the real world, are available on the project's website.

LM2D: Lyrics- and Music-Driven Dance Synthesis

Dance typically involves professional choreography with complex movements that follow a musical rhythm and can also be influenced by lyrical content. The integration of lyrics in addition to the auditory dimension, enriches the foundational tone and makes motion generation more amenable to its semantic meanings. However, existing dance synthesis methods tend to model motions only conditioned on audio signals. In this work, we make two contributions to bridge this gap. First, we propose LM2D, a novel probabilistic architecture that incorporates a multimodal diffusion model with consistency distillation, designed to create dance conditioned on both music and lyrics in one diffusion generation step. Second, we introduce the first 3D dance-motion dataset that encompasses both music and lyrics, obtained with pose estimation technologies. We evaluate our model against music-only baseline models with objective metrics and human evaluations, including dancers and choreographers. The results demonstrate LM2D is able to produce realistic and diverse dance matching both lyrics and music. A video summary can be accessed at: https://youtu.be/4XCgvYookvA.

Mind Meets Robots: A Review of EEG-Based Brain-Robot Interaction Systems

Brain-robot interaction (BRI) empowers individuals to control (semi-)automated machines through their brain activity, either passively or actively. In the past decade, BRI systems have achieved remarkable success, predominantly harnessing electroencephalogram (EEG) signals as the central component. This paper offers an up-to-date and exhaustive examination of 87 curated studies published during the last five years (2018-2023), focusing on identifying the research landscape of EEG-based BRI systems. This review aims to consolidate and underscore methodologies, interaction modes, application contexts, system evaluation, existing challenges, and potential avenues for future investigations in this domain. Based on our analysis, we present a BRI system model with three entities: Brain, Robot, and Interaction, depicting the internal relationships of a BRI system. We especially investigate the essence and principles on interaction modes between human brains and robots, a domain that has not yet been identified anywhere. We then discuss these entities with different dimensions encompassed. Within this model, we scrutinize and classify current research, reveal insights, specify challenges, and provide recommendations for future research trajectories in this field. Meanwhile, we envision our findings offer a design space for future human-robot interaction (HRI) research, informing the creation of efficient BRI frameworks.