Traccia

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 17

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Track-and-Complete: Learning Humanoid Skills from a Single Failed Human Video
Sep 29, 2026Sarmad Idrees, Jongeun ChoiHuman-To-Robot Transfer Traccia
School of Mechanical Engineering, Yonsei University, Seoul 03722, Korea
SlugTrails: An Egocentric Benchmark for Floor Plan Localization in Large Buildings
Sep 17, 2026Yunqian Cheng, Roberto ManduchiIndoor Localization Traccia
Department of Computer Science and Engineering University of California, Santa Cruz Santa Cruz, CA, USA
Detect Before You Attribute: Cascade Failure Attribution for Multi-Agent Systems
Aug 30, 2026Jiayi Zhang, Zexin Wang, Degang Sun +4Traccia
Computer Network Information Center, Chinese Academy of Sciences · Institute of Computing Technology, Chinese Academy of Sciences
Traceable Multi-Agent System for Knowledge-Based Forecasting
Aug 4, 2026Junhyeok Kang, Sangjun Han, Hyeokjun Choe +1Retrieval-Augmented Forecasting Forecast
LG AI Research · Seoul, Republic of Korea
TrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents
Aug 2, 2026Jingyu Sun, Yuyang Xue, Mingyang Li +9Traccia Single Trajectory
1The University of Manchester · 2The University of Melbourne · 3The University of Edinburgh +2
TRACTA: Benchmarking Temporal Reasoning over Semantic Trajectories
Jul 24, 2026Michael Romei De Socio, Gian Luca Pozzato, Alessio MerloTemporal Reasoning Neuro-Symbolic Framework
Department of Computer Science, University of Turin, Corso Svizzera 185, 10149 Turin, Italy · CASD – School of Advanced Defense Studies, Piazza della Rovere 83, 00165 Rome, Italy
Traccia: An OpenTelemetry-Based Governance Platform for AI Systems
Jul 15, 2026Nutan Kumar Naik, Aditya Kumar Saroj, Vijay Prasad Poudel +2Artificial Intelligence Governance Governance
Department of Earth and Atmospheric Sciences National Institute of Technology Rourkela Odisha, India · Algen.AI2026 Bengaluru, Karnataka, India
CLABTOOLKIT: An Open-Source Toolkit for Routine Processing, Manipulation, and Visualization of Neuroimaging Data
Jul 2, 2026Yasser Alemán-Gómez, Nino Hervé, Patric HagmannMultimodal Neuroimaging Visualization
Connectomics Lab, Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland · University of Lausanne (UNIL), Lausanne, Switzerland
TrajLoc: Trajectory-Attention Localization for Multi-Object Motion Control
Jul 1, 2026Omer Sela, Inbar Huberman-Spiegelglas, Michael Rotman +2Multi-Object Tracking Video Object Segmentation
Amazon Prime Video · Tel Aviv University
TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
Jun 27, 2026Liang Zhang, Gaojie Jin, Yao Shi +4Ego-Trajectory Prediction Pedestrian Trajectory Prediction
Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China · University of Chinese Academy of Sciences, China · Hangzhou Institute for Advanced Study, UCAS, China +1
TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
Jun 10, 2026Siyu Li, Toan Tran, Lingyi Zhao +2Mobility Traccia
Dept. of Computer Science, Emory University, Atlanta, GA, USA · Novateur Research Solutions, Ashburn, VA, USA
Tractogram foundation model
Jun 3, 2026Guikun Chen, Yuqian Chen, Yijie Li +5Functional Magnetic Resonance Imaging Traccia
The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310058, Zhejiang, China · Department of Radiology, Brigham and Women’s Hospital, Mass General Brigham, Boston 02115, Massachusetts, USA · Harvard Medical School, Boston 02115, Massachusetts, USA +4
Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation
Jun 1, 2026Donghwan Kim, Prakhar Singh, Younghoon Min +3Production Agentic Systems Multimodal Agents
The Pennsylvania State University · SK Hynix · KAIST
TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning
May 19, 2026Zhen Xiong, Shang-Ling Hsu, Cyrus ShahabiSpatial Tokens Traccia
University of Southern California
TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation
May 11, 2026Wilson Wongso, Lihuan Li, Arian Prabowo +4Student-Generated Trajectories Trajectory Generation
University of New South Wales · Hong Kong University of Science and Technology (Guangzhou)
TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
May 7, 2026Maria Despoina Siampou, Gengchen Mai, Ni Lao +4Urban Mobility Traccia
Google Research, Mountain View, CA · Dept. of Computer Science, University of Southern California, Los Angeles, CA · SEAI Lab, Dept. of Geography and the Environment, The University of Texas at Austin, Austin, TX +1
QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation
Apr 24, 2026Mathilde Kappel, Mahdi Khoramshahi, Louis Annabi +2Robot Trajectories Robotic Manipulation
Institute of Intelligent Systems and Robotics, CNRS, Sorbonne University, Paris, F-75005, France

Sep 29, 2026, cs.ROJ/K move · Enter open · S save

Track-and-Complete: Learning Humanoid Skills from a Single Failed Human Video

Sarmad Idrees, Jongeun Choi

School of Mechanical Engineering, Yonsei University, Seoul 03722, Korea

Human-To-Robot Transfer Traccia Motion

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Learning humanoid skills from videos typically requires a successful human demonstration, which often demands custom data collection. Although failures have traditionally been treated only as negative examples in robot learning, they can still reveal a usable trajectory prefix before the task fails, as well as the intended outcome. To leverage this information from a failed-attempt video, we propose TRACC, a pipeline that imitates the useful portion of the motion trajectory and then completes the task based on the inferred task outcome. The usable motion prefix serves as prior knowledge until the failure occurs, after which the task-completion reward guides the policy to learn the intended task goal without requiring a successful task trajectory. We evaluate our method on six in-the-wild failed human tasks from the Oops! dataset. Our experimental results demonstrate the effectiveness of the proposed approach for learning from failed attempts when no successful demonstration is available. Thus, these findings establish failed human videos as a viable source of supervision for humanoid skill learning.

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Latest papers 17