Search Results for author:
Found 12 papers, 6 papers with code
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 #2 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.
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.
