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.

My research interests include computer vision and robotics.

Email: wuruihai [at] pku.edu.cn

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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 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 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.