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Claim this profileWorks109 from public data
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control4,689
This work proposes a simple, general recipe to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
- Code as Policies: Language Model Programs for Embodied Control2,078
Code as Policies is presented, a robot-centric formulation of language model generated programs (LMPs) that can represent reactive policies (e.g., impedance controllers), as well as waypoint-based policies (vision-based pick and place, trajectory-based control), demonstrated across multiple real robot platforms.
- Multimodal Learning With Transformers: A Survey1,183
This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data, and a summary of the common challenges and designs shared by the multi-modal Transformer models and applications.
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- Structure analysis of soccer video with domain knowledge and hidden Markov models244
This paper defines two mutually exclusive states of the game, play and break based on the rules of soccer, and model the stochastic structures of each state of the game with a set of hidden Markov models.
- RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches188
This work proposes a policy conditioning method using rough trajectory sketches that is practical, easy to specify, and allows the policy to effectively perform new tasks that would otherwise be challenging to perform, and finds that RT-Trajectory is able to perform a wider range of tasks when provided the same training data.
- RoboVQA: Multimodal Long-Horizon Reasoning for Robotics176
A single video-conditioned model named RoboVQA-VideoCoCa trained on the dataset is demonstrated that is capable of performing a variety of grounded high-level reasoning tasks in broad realistic settings with a cognitive intervention rate 46% lower than the zeroshot state of the art visual language model (VLM) baseline and is able to guide real robots through long-horizon tasks.
- Deep Learning for Free-Hand Sketch: A Survey and A Toolbox160
A comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable.
- Advances in deep concealed scene understanding148
A comprehensive survey of deep learning techniques aimed at CSU, including a taxonomy, task-specific challenges, and ongoing developments is presented, and the largest and latest benchmark for concealed object segmentation (COS) is offered.
- Fault detection and identification using Bayesian recurrent neural networks147
A novel probabilistic fault detection and identification method is proposed which adopts a newly developed deep learning approach using Bayesian recurrent neural networks~(BRNNs) with variational dropout which yields uncertainty estimates which allow for simultaneous fault detection of chemical processes, direct fault identification, and fault propagation analysis.
- Landscape of the regulatory elements for lysine 2-hydroxyisobutyrylation pathway146
This study shows that Esa1p in budding yeast and its homologue Tip60 in human could add Khib to substrate proteins both in vitro and in vivo, and reports the first global profiling of Khib proteome in mammalian cells, identifying 6 548 Khib sites on 1 725 substrate proteins.
- AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents143
This paper proposes AutoRT, a system that leverages existing foundation models to scale up the deployment of operational robots in completely unseen scenarios with minimal human supervision, and leverages vision-language models (VLMs) for scene understanding and grounding, and further uses large language models (LLMs) for proposing diverse and novel instructions to be performed by a fleet of robots.
- SAM struggles in concealed scenes — empirical study on “Segment Anything”134
This report chooses three concealed scenes, i.e., camouflaged animals, industrial defects, and medical lesions, to evaluate SAM under unprompted settings, and observation is that SAM looks unskilled in concealed scenes.
- SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval130
This work proposes a deep hashing framework for sketch retrieval that, for the first time, works on a multi-million scale human sketch dataset and shows that state-of-the-art hashing models specifically engineered for static images fail to perform well on temporal sketch data.
- Predicting pipeline leakage in petrochemical system through GAN and LSTM108
A novel neural network based classification model using GAN and LSTM using a sliding window and a key feature selecting method in data preparation stage to address the noise and high dimension problems and is more suitable for fault prediction of pipeline leakage in petrochemical system.
- Multigraph Transformer for Free-Hand Sketch Recognition103
This work designs a novel graph neural network (GNN), the multigraph transformer (MGT), for learning representations of sketches from multiple graphs, which simultaneously capture global and local geometric stroke structures as well as temporal information.
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- PortraitBooth: A Versatile Portrait Model for Fast Identity-Preserved Personalization96
Por-traitBooth leverages subject embeddings from aface recognition model for personalized image generation without fine-tuning and eliminates computational overhead and mitigates identity distortion, and introduces dynamic identity preservation strategy, which ensures close resemblance to the original image identity.
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- Cross-modal subspace learning for fine-grained sketch-based image retrieval90
Through thorough examination of the experimental results, it is demonstrated that the subspace learning can effectively model the sketch-photo domain-gap, and a few key insights to drive future research are drawn.
- Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion83
Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.
- Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation77
A new class of implicit control policies combining the benefits of imitation learning with the robust handling of system constraints from Model Predictive Control (MPC) are presented, called Performer-MPC, which achieves 40% better goal reached in cluttered environments and 65% better on social metrics when navigating around humans.
- Crystal Structure of Enterovirus 71 RNA-Dependent RNA Polymerase Complexed with Its Protein Primer VPg: Implication for a trans Mechanism of VPg Uridylylation74
The crystal structure of EV71 3Dpol complexed with VPg is reported, demonstrating that different picornaviruses use distinct binding sites for VPg uridylylation.
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- BeHere: a VR/SAR remote collaboration system based on virtual replicas sharing gesture and avatar in a procedural task52
A Virtual Reality/Spatial Augmented Reality remote collaboration system based on 3D virtual replicas and sharing gestures and avatar, BeHere, which enables a remote expert in VR to guide a local worker in real-time to complete a procedural task in the real-world.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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