Search Results for author:
Found 8 papers, 3 papers with code
Learning Gradient Fields for Scalable and Generalizable Irregular Packing
In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling.
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene.
GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects.
Score-PA: Score-based 3D Part Assembly
In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly.
GenPose: Generative Category-level Object Pose Estimation via Diffusion Models
Object pose estimation plays a vital role in embodied AI and computer vision, enabling intelligent agents to comprehend and interact with their surroundings.
Ranked #1 on
6D Pose Estimation using RGBD
on REAL275
GFPose: Learning 3D Human Pose Prior with Gradient Fields
During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework.
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification
For object rearrangement, the TarGF can be used in two ways: 1) For model-based planning, we can cast the target gradient into a reference control and output actions with a distributed path planner; 2) For model-free reinforcement learning, the TarGF is not only used for estimating the likelihood-change as a reward but also provides suggested actions in residual policy learning.
Fully Decentralized Model-based Policy Optimization with Networked Agents
Model-based RL is an effective approach for reducing sample complexity.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
