1 citations · 2 across the 10 of their papers we have counts for
11 papers
FuncBridge: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning
Chuhao Zhou, Liquan Wang, Shuxin Cao +5
While humans readily repurpose a book, a stone, or a shoe to drive a nail, robots trained on specific tools fail to transfer the same function to novel ones -- a gap we formalize a…
Hierarchical Policy Learning via Spectral Decomposition
Shuxin Cao, Liquan Wang, Walker Byrnes +3
In this paper, we identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements. By analyzing actions in the spe…
ReSteer: Quantifying and Refining the Steerability of Multitask Robot Policies
Zhenyang Chen, Alan Tian, Liquan Wang +5
Despite strong multi-task pretraining, existing policies often exhibit poor task steerability. For example, a robot may fail to respond to a new instruction ``put the bowl in the s…
MosaicMem: Hybrid Spatial Memory for Controllable Video World Models
Wei Yu, Runjia Qian, Yumeng Li +8
Video diffusion models are moving beyond short, plausible clips toward world simulators that must remain consistent under camera motion, revisits, and intervention. Yet spatial mem…
Dexterous Manipulation Policies from RGB Human Videos via 3D Hand-Object Trajectory Reconstruction
Hongyi Chen, Tony Dong, Tiancheng Wu +7
Multi-finger robotic hand manipulation and grasping are challenging due to the high-dimensional action space and the difficulty of acquiring large-scale training data. Existing app…
TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies
Liquan Wang, Jiangjie Bian, Eric Heiden +1
Robotic manipulation tasks involving cutting deformable objects remain challenging due to complex topological behaviors, difficulties in perceiving dense object states, and the lac…
Discovering Robotic Interaction Modes with Discrete Representation Learning
Liquan Wang, Ankit Goyal, Haoping Xu +1
Human actions manipulating articulated objects, such as opening and closing a drawer, can be categorized into multiple modalities we define as interaction modes. Traditional robot…
Self-Supervised Learning of Action Affordances as Interaction Modes
Liquan Wang, Nikita Dvornik, Rafael Dubeau +2
When humans perform a task with an articulated object, they interact with the object only in a handful of ways, while the space of all possible interactions is nearly endless. This…
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands
Dylan Turpin, Liquan Wang, Eric Heiden +5
The study of hand-object interaction requires generating viable grasp poses for high-dimensional multi-finger models, often relying on analytic grasp synthesis which tends to produ…
GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels
Dylan Turpin, Liquan Wang, Stavros Tsogkas +2
Tool use requires reasoning about the fit between an object's affordances and the demands of a task. Visual affordance learning can benefit from goal-directed interaction experienc…
Generating Structured Adversarial Attacks Using Frank-Wolfe Method
Ehsan Kazemi, Thomas Kerdreux, Liquang Wang
White box adversarial perturbations are generated via iterative optimization algorithms most often by minimizing an adversarial loss on a neighborhood of the original imag…