Search Results for author:
Found 18 papers, 11 papers with code
Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization
Specifically, we construct a real-world dataset with 85 snowy videos, and then present a Semi-supervised Video Desnowing Network (SemiVDN) equipped by a novel Distribution-driven Contrastive Regularization.
Ranked #2 on
Snow Removal
on RVSD
(using extra training data)
GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
We present GR-2, a state-of-the-art generalist robot agent for versatile and generalizable robot manipulation.
World Model-based Perception for Visual Legged Locomotion
To address this issue, traditional methods attempt to learn a teacher policy with access to privileged information first and then learn a student policy to imitate the teacher's behavior with visual input.
GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned Policy
During training, GR-MG samples goal images from trajectories and conditions on both the text and the goal image or solely on the image when text is not available.
Ranked #1 on
Zero-shot Generalization
on CALVIN
(using extra training data)
RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining
Recently, the linear-complexity operator of the state space models (SSMs) has contrarily facilitated efficient long-term temporal modeling, which is crucial for rain streaks and raindrops removal in videos.
Ranked #1 on
Video deraining
on Video Waterdrop Removal Dataset
IRASim: Learning Interactive Real-Robot Action Simulators
We hope that IRASim can serve as an effective and scalable approach to enhance robot learning in the real world.
Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal
Although ViWS-Net is proposed to remove adverse weather conditions in videos with a single set of pre-trained weights, it is seriously blinded by seen weather at train-time and degenerates when coming to unseen weather during test-time.
Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation
In this paper, we extend the scope of this effectiveness by showing that visual robot manipulation can significantly benefit from large-scale video generative pre-training.
Ranked #5 on
Zero-shot Generalization
on CALVIN
(using extra training data)
Vision-Language Foundation Models as Effective Robot Imitators
We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy.
MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation
In this paper, we present a novel method for mobile manipulators to perform multiple contact-rich manipulation tasks.
Snow Removal in Video: A New Dataset and A Novel Method
We also develop a deep learning framework for video snow removal.
Ranked #4 on
Snow Removal
on RVSD
Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks
We propose a visual foresight model for pick-and-place rearrangement manipulation which is able to learn efficiently.
Put the Bear on the Chair! Intelligent Robot Interaction with Previously Unseen Chairs via Robot Imagination
Results show that our method enables the robot to autonomously seat the teddy bear on the 12 previously unseen chairs with a very high success rate.
LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration
In this paper, we propose a novel method called CPD with Local Surface Geometry (LSG-CPD) for rigid point cloud registration.
Can I Pour into It? Robot Imagining Open Containability Affordance of Previously Unseen Objects via Physical Simulations
In this letter, we propose a novel method for robots to "imagine" the open containability affordance of a previously unseen object via physical simulations.
MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization
Recently, large-scale datasets have vastly facilitated the development in nearly all domains of Natural Language Processing.
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer
We are able to create real stacks in 100% of trials with 61% efficiency and real rows in 100% of trials with 59% efficiency by directly loading the simulation-trained model on the real robot with no additional real-world fine-tuning.
Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body
In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object.
