16 citations · 26 across the 13 of their papers we have counts for
17 papers
DexForge: High-Fidelity Physics-Informed Dexterous Retargeting
Meizhong Wang, Kun Cao, Ruiqi Ni +2
Human demonstrations offer rich examples of precise dexterous manipulation and a promising source of robot training data. However, high-fidelity reproduction of demonstrated motion…
Faster Visuomotor Policy Learning on Action Manifolds via Riemannian MeanFlow
S. Talha Bukhari, Austin Garrett, Yi Wei +3
Visuomotor policies learn a direct map from raw sensory observations to robot action sequences. Policies based on Diffusion and Flow Matching capture the multimodal distribution ov…
Fast Generative Grasping via Lie Group-Constrained MeanFlow
S. Talha Bukhari, Yi Wei, Ruiqi Ni +2
Grasp synthesis is a core task in robotic manipulation, for which the solution typically forms a multimodal distribution rather than a point estimate. Generative robotic grasping a…
-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation
Xiaowei Cai, Yunuo Cai, Bingao Chen +36
Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-acti…
Weakly-supervised Learning for Physics-informed Neural Motion Planning via Sparse Roadmap
Ruiqi Ni, Yuchen Liu, Ahmed H. Qureshi
The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer…
Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning
Qingyi Chen, Ruiqi Ni, Junyoung Kim +1
Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots to navigate dynamic environment…
Physics-informed Value Learner for Offline Goal-Conditioned Reinforcement Learning
Vittorio Giammarino, Ruiqi Ni, Ahmed H. Qureshi
Offline Goal-Conditioned Reinforcement Learning (GCRL) holds great promise for domains such as autonomous navigation and locomotion, where collecting interactive data is costly and…
Physics-informed Neural Time Fields for Prehensile Object Manipulation
Hanwen Ren, Ruiqi Ni, Ahmed H. Qureshi
Object manipulation skills are necessary for robots operating in various daily-life scenarios, ranging from warehouses to hospitals. They allow the robots to manipulate the given o…
Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments
Yuchen Liu, Alexiy Buynitsky, Ruiqi Ni +1
Physics-informed Neural Motion Planners (PiNMPs) provide a data-efficient framework for solving the Eikonal Partial Differential Equation (PDE) and representing the cost-to-go func…
Physics-informed Temporal Difference Metric Learning for Robot Motion Planning
Ruiqi Ni, Zherong Pan, Ahmed H Qureshi
The motion planning problem involves finding a collision-free path from a robot's starting to its target configuration. Recently, self-supervised learning methods have emerged to t…
Physics-informed Neural Mapping and Motion Planning in Unknown Environments
Yuchen Liu, Ruiqi Ni, Ahmed H. Qureshi
Mapping and motion planning are two essential elements of robot intelligence that are interdependent in generating environment maps and navigating around obstacles. The existing ma…
Physics-informed Neural Motion Planning on Constraint Manifolds
Ruiqi Ni, Ahmed H. Qureshi
Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds. These problems appear in…
Progressive Learning for Physics-informed Neural Motion Planning
Ruiqi Ni, Ahmed H. Qureshi
Motion planning (MP) is one of the core robotics problems requiring fast methods for finding a collision-free robot motion path connecting the given start and goal states. Neural m…
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
Ruiqi Ni, Ahmed H. Qureshi
Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for lear…
Robust Multi-Robot Trajectory Optimization Using Alternating Direction Method of Multiplier
Ruiqi Ni, Zherong Pan, Xifeng Gao
We propose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimization into…
Multi-Robot Path Planning in Complex Environments via Graph Embedding
Xifeng Gao, Zherong Pan, Ruiqi Ni
We propose an approach to solve multi-agent path planning (MPP) problems for complex environments. Our method first designs a special pebble graph with a set of feasibility constra…
Robust & Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision
Ruiqi Ni, Teseo Schneider, Daniele Panozzo +2
Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits. We present the first local, optimization-b…