pwc-archive/papers-with-abstracts huggingface dataset:
Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian Process
Authors: Senming Tan, Zhenyu Hou, Zhihao Zhang, Long Xu, Mengke Zhang, Zhaoqi He, Chao Xu, Fei Gao, Yanjun Cao
Date: 2025-03-06 00:00:00
Tasks: Autonomous Navigation, Computational Efficiency, Gaussian Processes, GPU, Navigate

Terrain analysis is critical for the practical application of ground mobile robots in real-world tasks, especially in outdoor unstructured environments. In this paper, we propose a novel spatial-temporal traversability assessment method, which aims to enable autonomous robots to effectively navigate through complex terrains. Our approach utilizes sparse Gaussian processes (SGP) to extract geometric features (curvature, gradient, elevation, etc.) directly from point cloud scans. These features are then used to construct a high-resolution local traversability map. Then, we design a spatial-temporal Bayesian Gaussian kernel (BGK) inference method to dynamically evaluate traversability scores, integrating historical and real-time data while considering factors such as slope, flatness, gradient, and uncertainty metrics. GPU acceleration is applied in the feature extraction step, and the system achieves real-time performance. Extensive simulation experiments across diverse terrain scenarios demonstrate that our method outperforms SOTA approaches in both accuracy and computational efficiency. Additionally, we develop an autonomous navigation framework integrated with the traversability map and validate it with a differential driven vehicle in complex outdoor environments. Our code will be open-source for further research and development by the community, https://github.com/ZJU-FAST-Lab/FSGP_BGK.
GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping
Authors: Tao Zhong, Christine Allen-Blanchette
Date: 2025-03-06 00:00:00
Tasks: Grasp Generation

We propose GAGrasp, a novel framework for dexterous grasp generation that leverages geometric algebra representations to enforce equivariance to SE(3) transformations. By encoding the SE(3) symmetry constraint directly into the architecture, our method improves data and parameter efficiency while enabling robust grasp generation across diverse object poses. Additionally, we incorporate a differentiable physics-informed refinement layer, which ensures that generated grasps are physically plausible and stable. Extensive experiments demonstrate the model's superior performance in generalization, stability, and adaptability compared to existing methods. Additional details at https://gagrasp.github.io/
Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments
Authors: Beverley Gorry, Tobias Fischer, Michael Milford, Alejandro Fontan
Date: 2025-03-06 00:00:00
Tasks: Image Segmentation, Management, Semantic Segmentation, Visual Localization, Visual Place Recognition

Effective monitoring of underwater ecosystems is crucial for tracking environmental changes, guiding conservation efforts, and ensuring long-term ecosystem health. However, automating underwater ecosystem management with robotic platforms remains challenging due to the complexities of underwater imagery, which pose significant difficulties for traditional visual localization methods. We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images. This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes. Furthermore, we introduce the SQUIDLE+ VPR Benchmark-the first large-scale underwater VPR benchmark designed to leverage an extensive collection of unstructured data from multiple robotic platforms, spanning time intervals from days to years. The dataset encompasses diverse trajectories, arbitrary overlap and diverse seafloor types captured under varying environmental conditions, including differences in depth, lighting, and turbidity. Our code is available at: https://github.com/bev-gorry/underloc
Slug Mobile: Test-Bench for RL Testing
Authors: Jonathan Wellington Morris, Vishrut Shah, Alex Besanceney, Daksh Shah, Leilani H. Gilpin
Date: 2024-08-31 00:00:00
Tasks: Autonomous Vehicles

Sim-to real gap in Reinforcement Learning is when a model trained in a simulator does not translate to the real world. This is a problem for Autonomous Vehicles (AVs) as vehicle dynamics can vary from simulation to reality, and also from vehicle to vehicle. Slug Mobile is a one tenth scale autonomous vehicle created to help address the sim-to-real gap for AVs by acting as a test-bench to develop models that can easily scale from one vehicle to another. In addition to traditional sensors found in other one tenth scale AVs, we have also included a Dynamic Vision Sensor so we can train Spiking Neural Networks running on neuromorphic hardware.
Instrument-Splatting: Controllable Photorealistic Reconstruction of Surgical Instruments Using Gaussian Splatting
Authors: Shuojue Yang, Zijian Wu, Mingxuan Hong, Qian Li, Daiyun Shen, Septimiu E. Salcudean, Yueming Jin
Date: 2025-03-06 00:00:00
Tasks: 3D Reconstruction, Pose Tracking

Real2Sim is becoming increasingly important with the rapid development of surgical artificial intelligence (AI) and autonomy. In this work, we propose a novel Real2Sim methodology, Instrument-Splatting, that leverages 3D Gaussian Splatting to provide fully controllable 3D reconstruction of surgical instruments from monocular surgical videos. To maintain both high visual fidelity and manipulability, we introduce a geometry pre-training to bind Gaussian point clouds on part mesh with accurate geometric priors and define a forward kinematics to control the Gaussians as flexible as real instruments. Afterward, to handle unposed videos, we design a novel instrument pose tracking method leveraging semantics-embedded Gaussians to robustly refine per-frame instrument poses and joint states in a render-and-compare manner, which allows our instrument Gaussian to accurately learn textures and reach photorealistic rendering. We validated our method on 2 publicly released surgical videos and 4 videos collected on ex vivo tissues and green screens. Quantitative and qualitative evaluations demonstrate the effectiveness and superiority of the proposed method.
Enhancing Autonomous Driving Safety with Collision Scenario Integration
Authors: Zi Wang, Shiyi Lan, Xinglong Sun, Nadine Chang, Zhenxin Li, Zhiding Yu, Jose M. Alvarez
Date: 2025-03-05 00:00:00
Tasks: Autonomous Driving, Collision Avoidance, Imitation Learning, Self-Driving Cars

Autonomous vehicle safety is crucial for the successful deployment of self-driving cars. However, most existing planning methods rely heavily on imitation learning, which limits their ability to leverage collision data effectively. Moreover, collecting collision or near-collision data is inherently challenging, as it involves risks and raises ethical and practical concerns. In this paper, we propose SafeFusion, a training framework to learn from collision data. Instead of over-relying on imitation learning, SafeFusion integrates safety-oriented metrics during training to enable collision avoidance learning. In addition, to address the scarcity of collision data, we propose CollisionGen, a scalable data generation pipeline to generate diverse, high-quality scenarios using natural language prompts, generative models, and rule-based filtering. Experimental results show that our approach improves planning performance in collision-prone scenarios by 56\% over previous state-of-the-art planners while maintaining effectiveness in regular driving situations. Our work provides a scalable and effective solution for advancing the safety of autonomous driving systems.
COARSE: Collaborative Pseudo-Labeling with Coarse Real Labels for Off-Road Semantic Segmentation
Authors: Aurelio Noca, Xianmei Lei, Jonathan Becktor, Jeffrey Edlund, Anna Sabel, Patrick Spieler, Curtis Padgett, Alexandre Alahi, Deegan Atha
Date: 2025-03-05 00:00:00
Tasks: Domain Adaptation, Semantic Segmentation, Semi-supervised Domain Adaptation

Autonomous off-road navigation faces challenges due to diverse, unstructured environments, requiring robust perception with both geometric and semantic understanding. However, scarce densely labeled semantic data limits generalization across domains. Simulated data helps, but introduces domain adaptation issues. We propose COARSE, a semi-supervised domain adaptation framework for off-road semantic segmentation, leveraging sparse, coarse in-domain labels and densely labeled out-of-domain data. Using pretrained vision transformers, we bridge domain gaps with complementary pixel-level and patch-level decoders, enhanced by a collaborative pseudo-labeling strategy on unlabeled data. Evaluations on RUGD and Rellis-3D datasets show significant improvements of 9.7\% and 8.4\% respectively, versus only using coarse data. Tests on real-world off-road vehicle data in a multi-biome setting further demonstrate COARSE's applicability.
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
Authors: Arthur Zhang, Harshit Sikchi, Amy Zhang, Joydeep Biswas
Date: 2025-03-05 00:00:00
Tasks: Active Learning, counterfactual, Navigate

We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to achieving this is learning perceptual representations that generalize to open-set factors (e.g. novel semantic classes, terrains, dynamic entities) and inferring expert-aligned navigation costs from limited demonstrations. CREStE addresses both these issues, introducing 1) a visual foundation model (VFM) distillation objective for learning open-set structured bird's-eye-view perceptual representations, and 2) counterfactual inverse reinforcement learning (IRL), a novel active learning formulation that uses counterfactual trajectory demonstrations to reason about the most important cues when inferring navigation costs. We evaluate CREStE on the task of kilometer-scale mapless navigation in a variety of city, offroad, and residential environments and find that it outperforms all state-of-the-art approaches with 70% fewer human interventions, including a 2-kilometer mission in an unseen environment with just 1 intervention; showcasing its robustness and effectiveness for long-horizon mapless navigation. Videos and additional materials can be found on the project page: https://amrl.cs.utexas.edu/creste
LensDFF: Language-enhanced Sparse Feature Distillation for Efficient Few-Shot Dexterous Manipulation
Authors: Qian Feng, David S. Martinez Lema, Jianxiang Feng, Zhaopeng Chen, Alois Knoll
Date: 2025-03-05 00:00:00
Tasks: NeRF, Neural Rendering

Learning dexterous manipulation from few-shot demonstrations is a significant yet challenging problem for advanced, human-like robotic systems. Dense distilled feature fields have addressed this challenge by distilling rich semantic features from 2D visual foundation models into the 3D domain. However, their reliance on neural rendering models such as Neural Radiance Fields (NeRF) or Gaussian Splatting results in high computational costs. In contrast, previous approaches based on sparse feature fields either suffer from inefficiencies due to multi-view dependencies and extensive training or lack sufficient grasp dexterity. To overcome these limitations, we propose Language-ENhanced Sparse Distilled Feature Field (LensDFF), which efficiently distills view-consistent 2D features onto 3D points using our novel language-enhanced feature fusion strategy, thereby enabling single-view few-shot generalization. Based on LensDFF, we further introduce a few-shot dexterous manipulation framework that integrates grasp primitives into the demonstrations to generate stable and highly dexterous grasps. Moreover, we present a real2sim grasp evaluation pipeline for efficient grasp assessment and hyperparameter tuning. Through extensive simulation experiments based on the real2sim pipeline and real-world experiments, our approach achieves competitive grasping performance, outperforming state-of-the-art approaches.
Pretrained LLMs as Real-Time Controllers for Robot Operated Serial Production Line
Authors: Muhammad Waseem, Kshitij Bhatta, Chen Li, Qing Chang
Date: 2025-03-05 00:00:00
Tasks: Cloud Computing, Multi-agent Reinforcement Learning, Scheduling

The manufacturing industry is undergoing a transformative shift, driven by cutting-edge technologies like 5G, AI, and cloud computing. Despite these advancements, effective system control, which is crucial for optimizing production efficiency, remains a complex challenge due to the intricate, knowledge-dependent nature of manufacturing processes and the reliance on domain-specific expertise. Conventional control methods often demand heavy customization, considerable computational resources, and lack transparency in decision-making. In this work, we investigate the feasibility of using Large Language Models (LLMs), particularly GPT-4, as a straightforward, adaptable solution for controlling manufacturing systems, specifically, mobile robot scheduling. We introduce an LLM-based control framework to assign mobile robots to different machines in robot assisted serial production lines, evaluating its performance in terms of system throughput. Our proposed framework outperforms traditional scheduling approaches such as First-Come-First-Served (FCFS), Shortest Processing Time (SPT), and Longest Processing Time (LPT). While it achieves performance that is on par with state-of-the-art methods like Multi-Agent Reinforcement Learning (MARL), it offers a distinct advantage by delivering comparable throughput without the need for extensive retraining. These results suggest that the proposed LLM-based solution is well-suited for scenarios where technical expertise, computational resources, and financial investment are limited, while decision transparency and system scalability are critical concerns.
Combined Physics and Event Camera Simulator for Slip Detection
Authors: Thilo Reinold, Suman Ghosh, Guillermo Gallego
Date: 2025-03-05 00:00:00
Tasks: Robot Manipulation

Robot manipulation is a common task in fields like industrial manufacturing. Detecting when objects slip from a robot's grasp is crucial for safe and reliable operation. Event cameras, which register pixel-level brightness changes at high temporal resolution (called ``events''), offer an elegant feature when mounted on a robot's end effector: since they only detect motion relative to their viewpoint, a properly grasped object produces no events, while a slipping object immediately triggers them. To research this feature, representative datasets are essential, both for analytic approaches and for training machine learning models. The majority of current research on slip detection with event-based data is done on real-world scenarios and manual data collection, as well as additional setups for data labeling. This can result in a significant increase in the time required for data collection, a lack of flexibility in scene setups, and a high level of complexity in the repetition of experiments. This paper presents a simulation pipeline for generating slip data using the described camera-gripper configuration in a robot arm, and demonstrates its effectiveness through initial data-driven experiments. The use of a simulator, once it is set up, has the potential to reduce the time spent on data collection, provide the ability to alter the setup at any time, simplify the process of repetition and the generation of arbitrarily large data sets. Two distinct datasets were created and validated through visual inspection and artificial neural networks (ANNs). Visual inspection confirmed photorealistic frame generation and accurate slip modeling, while three ANNs trained on this data achieved high validation accuracy and demonstrated good generalization capabilities on a separate test set, along with initial applicability to real-world data. Project page: https://github.com/tub-rip/event_slip
An Unsupervised C-Uniform Trajectory Sampler with Applications to Model Predictive Path Integral Control
Authors: O. Goktug Poyrazoglu, Rahul Moorthy, Yukang Cao, William Chastek, Volkan Isler
Date: 2025-03-04 00:00:00

Sampling-based model predictive controllers generate trajectories by sampling control inputs from a fixed, simple distribution such as the normal or uniform distributions. This sampling method yields trajectory samples that are tightly clustered around a mean trajectory. This clustering behavior in turn, limits the exploration capability of the controller and reduces the likelihood of finding feasible solutions in complex environments. Recent work has attempted to address this problem by either reshaping the resulting trajectory distribution or increasing the sample entropy to enhance diversity and promote exploration. In our recent work, we introduced the concept of C-Uniform trajectory generation [1] which allows the computation of control input probabilities to generate trajectories that sample the configuration space uniformly. In this work, we first address the main limitation of this method: lack of scalability due to computational complexity. We introduce Neural C-Uniform, an unsupervised C-Uniform trajectory sampler that mitigates scalability issues by computing control input probabilities without relying on a discretized configuration space. Experiments show that Neural C-Uniform achieves a similar uniformity ratio to the original C-Uniform approach and generates trajectories over a longer time horizon while preserving uniformity. Next, we present CU-MPPI, which integrates Neural C-Uniform sampling into existing MPPI variants. We analyze the performance of CU-MPPI in simulation and real-world experiments. Our results indicate that in settings where the optimal solution has high curvature, CU-MPPI leads to drastic improvements in performance.
Closing the Intent-to-Behavior Gap via Fulfillment Priority Logic
Authors: Bassel El Mabsout, Abdelrahman Abdelgawad, Renato Mancuso
Date: 2025-03-04 00:00:00
Tasks: continuous-control, Continuous Control, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Practitioners designing reinforcement learning policies face a fundamental challenge: translating intended behavioral objectives into representative reward functions. This challenge stems from behavioral intent requiring simultaneous achievement of multiple competing objectives, typically addressed through labor-intensive linear reward composition that yields brittle results. Consider the ubiquitous robotics scenario where performance maximization directly conflicts with energy conservation. Such competitive dynamics are resistant to simple linear reward combinations. In this paper, we present the concept of objective fulfillment upon which we build Fulfillment Priority Logic (FPL). FPL allows practitioners to define logical formula representing their intentions and priorities within multi-objective reinforcement learning. Our novel Balanced Policy Gradient algorithm leverages FPL specifications to achieve up to 500\% better sample efficiency compared to Soft Actor Critic. Notably, this work constitutes the first implementation of non-linear utility scalarization design, specifically for continuous control problems.
GraphGarment: Learning Garment Dynamics for Bimanual Cloth Manipulation Tasks
Authors: Wei Chen, Kelin Li, Dongmyoung Lee, Xiaoshuai Chen, Rui Zong, Petar Kormushev
Date: 2025-03-04 00:00:00
Tasks: Graph Neural Network

Physical manipulation of garments is often crucial when performing fabric-related tasks, such as hanging garments. However, due to the deformable nature of fabrics, these operations remain a significant challenge for robots in household, healthcare, and industrial environments. In this paper, we propose GraphGarment, a novel approach that models garment dynamics based on robot control inputs and applies the learned dynamics model to facilitate garment manipulation tasks such as hanging. Specifically, we use graphs to represent the interactions between the robot end-effector and the garment. GraphGarment uses a graph neural network (GNN) to learn a dynamics model that can predict the next garment state given the current state and input action in simulation. To address the substantial sim-to-real gap, we propose a residual model that compensates for garment state prediction errors, thereby improving real-world performance. The garment dynamics model is then applied to a model-based action sampling strategy, where it is utilized to manipulate the garment to a reference pre-hanging configuration for garment-hanging tasks. We conducted four experiments using six types of garments to validate our approach in both simulation and real-world settings. In simulation experiments, GraphGarment achieves better garment state prediction performance, with a prediction error 0.46 cm lower than the best baseline. Our approach also demonstrates improved performance in the garment-hanging simulation experiment with enhancements of 12%, 24%, and 10%, respectively. Moreover, real-world robot experiments confirm the robustness of sim-to-real transfer, with an error increase of 0.17 cm compared to simulation results. Supplementary material is available at:https://sites.google.com/view/graphgarment.
Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical Music
Authors: Eetu Tunturi, David Diaz-Guerra, Archontis Politis, Tuomas Virtanen
Date: 2025-03-10 00:00:00
Tasks: Music Source Separation

Music source separation is the task of separating a mixture of instruments into constituent tracks. Music source separation models are typically trained using only audio data, although additional information can be used to improve the model's separation capability. In this paper, we propose two ways of using musical scores to aid music source separation: a score-informed model where the score is concatenated with the magnitude spectrogram of the audio mixture as the input of the model, and a model where we use only the score to calculate the separation mask. We train our models on synthetic data in the SynthSOD dataset and evaluate our methods on the URMP and Aalto anechoic orchestra datasets, comprised of real recordings. The score-informed model improves separation results compared to a baseline approach, but struggles to generalize from synthetic to real data, whereas the score-only model shows a clear improvement in synthetic-to-real generalization.