Ruihai Wu

I am a fourth-year PhD Candidate in Center on Frontiers of Computing Studies (CFCS) at Peking University. , advised by Professor Hao Dong. Currently, I am a visiting PhD student at University of Illinois Urbana-Champaign (UIUC), advised by Professor Yunzhu Li.

My research interests include computer vision and robotics.

Email: wuruihai [at] pku.edu.cn

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We formulate interactive exploration as an action-conditioned 3D scene graph (ACSG) construction and traversal problem. Our ACSG is an actionable, spatial-topological representation that models objects and their interactive and spatial relations in a scene, capturing both the high-level graph and corresponding low-level memory.

We propose to learn dense visual correspondence for diverse garment manipulation tasks with category-level generalization using only one- or few-shot human demonstrations.

We study geometric shape assembly by leveraging SE(3) Equivariance, which disentangles poses and shapes of fractured parts.

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 longterm manipulation.

We study collaborative affordance for dual-gripper manipulation. The core is to reduce the quadratic problem for two grippers into two disentangled yet interconnected subtasks.

We introduce a novel framework that explicitly disentangles the part motion of articulated objects by predicting the movements of articulated parts by utilizing spatially continuous neural implicit representations.

We study performing very few test-time interactions for quickly adapting the affordance priors to more accurate instance-specific posteriors.

We study dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals for manipulating articulated objects.

We train a forward model from video data only, via disentangling the motion of controllable agent to model the transition dynamics.

We propose adversarial consistency loss for image-to-image translation that does not require the translated image to be translated back to the source image.

We propose localize-assemble-predicate network (LAP-Net), decomposing visual relation detection (VRD) into three sub-tasks to tackle the long-tailed data distribution problem.

We introduce prototype learning, partial matching and convolution layers with top-down modulation into feature extraction to purposefully reduce the contamination by occlusion.