Ruihai Wu

Postdoc Scholar at UC Berkeley. Doctor of Philosophy at Peking University.

Email: wuruihai [at] pku.edu.cn

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We propose to learn point-level affordance to model the complex space and multi-modal manipulation candidates of garment piles, with novel designs for the awareness of garment geometry, structure, inter-object relations, and further adaptation.

We exploit the geometric generalization of point-level affordance, learning affordance aware of bimanual collaboration in geometric assembly with long-horizon action sequences.

We propose AdaRPG, a novel framework that leverages foundation models to extract object parts, which exhibit greater local geometric similarity than entire objects, thereby enhancing visual affordance generalization for functional primitive skills.

We propose adaptive assets, environment and policy to manipulate articulated objects with diverse mechanisms.

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.

We propose a novel framework that enables VLM to understand human-designed manuals and acquire robotic skills for furniture assembly tasks.

We propose RoboVerse, a comprehensive framework for advancing robotics through a simulation platform, synthetic dataset, and unified benchmarks. Its MetaSim infrastructure abstracts diverse simulators into a universal interface, ensuring interoperability and extensibility.

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

In this paper, we study retrieving objects in complicated clutters via a novel method of recursively broadcasting the accurate local dynamics to build a support relation graph of the whole scene, which largely reduces the complexity of the support relation inference and improves the accuracy.

We present GarmentLab, a benchmark designed for garment manipulation within realistic 3D indoor scenes. Our benchmark encompasses a diverse range of garment types, robotic systems and manipulators including dexterous hands. The multitude of tasks included in the benchmark enables further exploration of the interactions between garments, deformable objects, rigid bodies, fluids, and avatars.

PreAfford is a novel pre-grasping planning framework that improves adaptability across diverse environments and object by utilizing point-level affordance representation and relay training. Validated on the ShapeNet-v2 dataset and real-world experiments, PreAfford offers a robust solution for manipulating ungraspable objects with two-finger grippers.

We introduce a comprehensive benchmark, NaturalVLM, comprising 15 distinct manipulation tasks, containing over 4500 episodes meticulously annotated with fine-grained language instructions. Besides, we propose a novel learning framework that completes the manipulation task step-by-step according to the fine-grained instructions.

We explore the task of manipulating articulated objects within environment constraints and formulate the task of environment-aware affordance learning for manipulating 3D articulated objects, incorporating object-centric per-point priors and environment constraints.

We introduce an affordance learning framework that effectively explores novel categories with minimal interactions on a limited number of instances. 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.

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.