Search Results for author:
Found 16 papers, 4 papers with code
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy
We theoretically extend equivariant Markov kernels and simplify the condition of equivariant diffusion process, thereby significantly improving training efficiency for trajectory-level SE(3) equivariant diffusion policy in an end-to-end manner.
GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation
Recent successes in reinforcement learning and vision-based methods offer promising avenues for learning garment manipulation.
EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning
Humans perceive and interact with the world with the awareness of equivariance, facilitating us in manipulating different objects in diverse poses.
UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence
Garment manipulation (e. g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity of garment configurations, geometries and deformations.
PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments
Robotic manipulation with two-finger grippers is challenged by objects lacking distinct graspable features.
NaturalVLM: Leveraging Fine-grained Natural Language for Affordance-Guided Visual Manipulation
To address these challenges, we introduce a comprehensive benchmark, NrVLM, comprising 15 distinct manipulation tasks, containing over 4500 episodes meticulously annotated with fine-grained language instructions.
RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation
We introduce the novel task of interactive scene exploration, wherein robots autonomously explore environments and produce an action-conditioned scene graph (ACSG) that captures the structure of the underlying environment.
Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations
More importantly, while many methods could only model a certain kind of joint motion (such as the revolution in the clockwise order), our proposed framework is generic to different kinds of joint motions in that transformation matrix can model diverse kinds of joint motions in the space.
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Our framework explicitly estimates the geometric similarity across different categories, identifying local areas that differ from shapes in the training categories for efficient exploration while concurrently transferring affordance knowledge to similar parts of the objects.
Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly
Shape assembly aims to reassemble parts (or fragments) into a complete object, which is a common task in our daily life.
Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation
In this paper, we study deformable object manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel kind of foresightful dense affordance, which avoids local optima by estimating states' values for long-term manipulation.
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation
It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments.
AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments.
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
Unpaired Image-to-Image Translation using Adversarial Consistency Loss
Cycle-consistency loss is a widely used constraint for such problems.
TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion
Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.
