arXiv subjects

Emma Li

Publications and source records attributed to Emma Li.

7 recordsLinked to original sources

arXiv2025

ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration

In collaborative human-robot tasks, safety requires not only avoiding collisions but also ensuring safe, intentional physical contact. We present ContactRL, a reinforcement learning (RL) based framework that directly incorporates contact safety into the reward function through force feedback. This enables a robot to learn adaptive motion profiles that minimize human-robot contact forces while maintaining task efficiency. In simulation, ContactRL achieves a low safety violation rate of 0.2\% with a high task success rate of 87.7\%, outperforming state-of-the-art constrained RL baselines. In order to guarantee deployment safety, we augment the learned policy with a kinetic energy based Control Barrier Function (eCBF) shield. Real-world experiments on an UR3e robotic platform performing small object handovers from a human hand across 360 trials confirm safe contact, with measured normal forces consistently below 10N. These results demonstrate that ContactRL enables safe and efficient physical collaboration, thereby advancing the deployment of collaborative robots in contact-rich tasks.

cs.RO↗

arXiv2025

Task-Oriented Communications for 3D Scene Representation: Balancing Timeliness and Fidelity

Real-time Three-dimensional (3D) scene representation is a foundational element that supports a broad spectrum of cutting-edge applications, including digital manufacturing, Virtual, Augmented, and Mixed Reality (VR/AR/MR), and the emerging metaverse. Despite advancements in real-time communication and computing, achieving a balance between timeliness and fidelity in 3D scene representation remains a challenge. This work investigates a wireless network where multiple homogeneous mobile robots, equipped with cameras, capture an environment and transmit images to an edge server over channels for 3D representation. We propose a contextual-bandit Proximal Policy Optimization (PPO) framework incorporating both Age of Information (AoI) and semantic information to optimize image selection for representation, balancing data freshness and representation quality. Two policies -- the $\omega$-threshold and $\omega$-wait policies -- together with two benchmark methods are evaluated, timeliness embedding and weighted sum, on standard datasets and baseline 3D scene representation models. Experimental results demonstrate improved representation fidelity while maintaining low latency, offering insight into the model's decision-making process. This work advances real-time 3D scene representation by optimizing the trade-off between timeliness and fidelity in dynamic environments.

cs.CV↗

arXiv2025

Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse

Real-time human-device interaction in industrial Metaverse faces challenges such as high computational load, limited bandwidth, and strict latency. This paper proposes a task-oriented edge-assisted cross-system framework using digital twins (DTs) to enable responsive interactions. By predicting operator motions, the system supports: 1) proactive Metaverse rendering for visual feedback, and 2) preemptive control of remote devices. The DTs are decoupled into two virtual functions-visual display and robotic control-optimizing both performance and adaptability. To enhance generalizability, we introduce the Human-In-The-Loop Model-Agnostic Meta-Learning (HITL-MAML) algorithm, which dynamically adjusts prediction horizons. Evaluation on two tasks demonstrates the framework's effectiveness: in a Trajectory-Based Drawing Control task, it reduces weighted RMSE from 0.0712 m to 0.0101 m; in a real-time 3D scene representation task for nuclear decommissioning, it achieves a PSNR of 22.11, SSIM of 0.8729, and LPIPS of 0.1298. These results show the framework's capability to ensure spatial precision and visual fidelity in real-time, high-risk industrial environments.

cs.RO↗

arXiv2025

Preference-Driven Active 3D Scene Representation for Robotic Inspection in Nuclear Decommissioning

Active 3D scene representation is pivotal in modern robotics applications, including remote inspection, manipulation, and telepresence. Traditional methods primarily optimize geometric fidelity or rendering accuracy, but often overlook operator-specific objectives, such as safety-critical coverage or task-driven viewpoints. This limitation leads to suboptimal viewpoint selection, particularly in constrained environments such as nuclear decommissioning. To bridge this gap, we introduce a novel framework that integrates expert operator preferences into the active 3D scene representation pipeline. Specifically, we employ Reinforcement Learning from Human Feedback (RLHF) to guide robotic path planning, reshaping the reward function based on expert input. To capture operator-specific priorities, we conduct interactive choice experiments that evaluate user preferences in 3D scene representation. We validate our framework using a UR3e robotic arm for reactor tile inspection in a nuclear decommissioning scenario. Compared to baseline methods, our approach enhances scene representation while optimizing trajectory efficiency. The RLHF-based policy consistently outperforms random selection, prioritizing task-critical details. By unifying explicit 3D geometric modeling with implicit human-in-the-loop optimization, this work establishes a foundation for adaptive, safety-critical robotic perception systems, paving the way for enhanced automation in nuclear decommissioning, remote maintenance, and other high-risk environments.

cs.RO↗

arXiv2024

Active Listener: Continuous Generation of Listener's Head Motion Response in Dyadic Interactions

A key component of dyadic spoken interactions is the contextually relevant non-verbal gestures, such as head movements that reflect a listener's response to the interlocutor's speech. Although significant progress has been made in the context of generating co-speech gestures, generating listener's response has remained a challenge. We introduce the task of generating continuous head motion response of a listener in response to the speaker's speech in real time. To this end, we propose a graph-based end-to-end crossmodal model that takes interlocutor's speech audio as input and directly generates head pose angles (roll, pitch, yaw) of the listener in real time. Different from previous work, our approach is completely data-driven, does not require manual annotations or oversimplify head motion to merely nods and shakes. Extensive evaluation on the dyadic interaction sessions on the IEMOCAP dataset shows that our model produces a low overall error (4.5 degrees) and a high frame rate, thereby indicating its deployability in real-world human-robot interaction systems. Our code is available at - https://github.com/bigzen/Active-Listener

cs.RO↗

arXiv2024

Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset

We introduce HARPER, a novel dataset for 3D body pose estimation and forecast in dyadic interactions between users and Spot, the quadruped robot manufactured by Boston Dynamics. The key-novelty is the focus on the robot's perspective, i.e., on the data captured by the robot's sensors. These make 3D body pose analysis challenging because being close to the ground captures humans only partially. The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users. The Corpus contains not only the recordings of the built-in stereo cameras of Spot, but also those of a 6-camera OptiTrack system (all recordings are synchronized). This leads to ground-truth skeletal representations with a precision lower than a millimeter. In addition, the Corpus includes reproducible benchmarks on 3D Human Pose Estimation, Human Pose Forecasting, and Collision Prediction, all based on publicly available baseline approaches. This enables future HARPER users to rigorously compare their results with those we provide in this work.

cs.CV↗

arXiv2023

Understanding Dynamic Human-Robot Proxemics in the Case of Four-Legged Canine-Inspired Robots

The integration of humanoid and animal-shaped robots into specialized domains, such as healthcare, multi-terrain operations, and psychotherapy, necessitates a deep understanding of proxemics--the study of spatial behavior that governs effective human-robot interactions. Unlike traditional robots in manufacturing or logistics, these robots must navigate complex human environments where maintaining appropriate physical and psychological distances is crucial for seamless interaction. This study explores the application of proxemics in human-robot interactions, focusing specifically on quadruped robots, which present unique challenges and opportunities due to their lifelike movement and form. Utilizing a motion capture system, we examine how different interaction postures of a canine robot influence human participants' proxemic behavior in dynamic scenarios. By capturing and analyzing position and orientation data, this research aims to identify key factors that affect proxemic distances and inform the design of socially acceptable robots. The findings underscore the importance of adhering to human psychological and physical distancing norms in robot design, ensuring that autonomous systems can coexist harmoniously with humans.

cs.HC↗