165 citations · 332 across the 61 of their papers we have counts for
64 papers
Collective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation
Shaocheng Liu, Yuan Meng, Zhenshan Bing +1
Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodiment-dependent transition dynam…
VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness
Hang Li, Mingxin Zhang, Zihan Wu +9
Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rely on constrained camera view…
C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video
Jie Ren, Zhehao Jiang, Yinhong Yang +9
High-quality demonstrations for dexterous robot manipulation are costly and difficult to collect, whereas monocular human videos provide a scalable source of diverse manipulation b…
Knowledge Reutilization in Meta-Reinforcement Learning
Yuan Meng, Bo Wang, Juan de los Rios Ruiz +4
Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-s…
Dreaming the Unseen: World Model-regularized Diffusion Policy for Out-of-Distribution Robustness
Ziou Hu, Xiangtong Yao, Yuan Meng +2
Diffusion policies excel at visuomotor control but often fail catastrophically under severe out-of-distribution (OOD) disturbances, such as unexpected object displacements or visua…
ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries
Hang Li, Fengyi Shen, Dong Chen +6
Vision-language-action (VLA) models for closed-loop robot control are typically cast under the Markov assumption, making them prone to errors on tasks requiring historical context.…
From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
Ju Dong, Liding Zhang, Lei Zhang +7
Generative policies based on diffusion and flow matching achieve strong performance in robotic manipulation by modeling multi-modal human demonstrations. However, their reliance on…
MOSAIC: Bridging the Sim-to-Real Gap in Generalist Humanoid Motion Tracking and Teleoperation with Rapid Residual Adaptation
Zhenguo Sun, Bo-Sheng Huang, Yibo Peng +9
Generalist humanoid motion trackers have recently achieved strong simulation metrics by scaling data and training, yet often remain brittle on hardware during sustained teleoperati…
DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter
Xukun Li, Yu Sun, Lei Zhang +10
Bimanual dexterous manipulation relies on integrating multimodal inputs to perform complex real-world tasks. To address the challenges of effectively combining these modalities, we…
TacUMI: A Multi-Modal Universal Manipulation Interface for Contact-Rich Tasks
Tailai Cheng, Kejia Chen, Lingyun Chen +8
Task decomposition is critical for understanding and learning complex long-horizon manipulation tasks. Especially for tasks involving rich physical interactions, relying solely on…
OmniDexVLG: Learning Dexterous Grasp Generation from Vision Language Model-Guided Grasp Semantics, Taxonomy and Functional Affordance
Lei Zhang, Diwen Zheng, Kaixin Bai +5
Dexterous grasp generation aims to produce grasp poses that align with task requirements and human interpretable grasp semantics. However, achieving semantically controllable dexte…
DualGazeNet: A Biologically Inspired Dual-Gaze Query Network for Salient Object Detection
Yu Zhang, Haoan Ping, Yuchen Li +3
Recent salient object detection (SOD) methods aim to improve performance in four key directions: semantic enhancement, boundary refinement, auxiliary task supervision, and multi-mo…
Robotic Ultra-Long-Horizon Manipulation Skills via Human-guided Lifelong Code Generation
Yuan Meng, Zhenguo Sun, Max Fest +4
Large language models (LLMs) can translate natural-language instructions for robotic manipulation into executable code, but ambiguity, noisy generations, and limited context window…
Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios
Xiangtong Yao, Yirui Zhou, Yuan Meng +7
Diffusion policies are powerful visuomotor models for robotic manipulation, yet they often fail to generalize to manipulators or end-effectors unseen during training and struggle t…
RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking
Zhenguo Sun, Yibo Peng, Yuan Meng +5
Long-horizon, high-dynamic motion tracking on humanoids remains brittle: retargeted reference motions are typically kinematically plausible but dynamically inconsistent with the ro…
FUNCanon: Learning Pose-Aware Action Primitives via Functional Object Canonicalization for Generalizable Robotic Manipulation
Hongli Xu, Lei Zhang, Xiaoyue Hu +7
General-purpose robotic skills from end-to-end demonstrations often leads to task-specific policies that fail to generalize beyond the training distribution. Therefore, we introduc…
M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation
Ju Dong, Lei Zhang, Liding Zhang +8
Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. E…
Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler
Liding Zhang, Kuanqi Cai, Yu Zhang +5
Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-…
Language-Enhanced Mobile Manipulation for Efficient Object Search in Indoor Environments
Liding Zhang, Zeqi Li, Kuanqi Cai +3
Enabling robots to efficiently search for and identify objects in complex, unstructured environments is critical for diverse applications ranging from household assistance to indus…
Deep Fuzzy Optimization for Batch-Size and Nearest Neighbors in Optimal Robot Motion Planning
Liding Zhang, Qiyang Zong, Yu Zhang +2
Efficient motion planning algorithms are essential in robotics. Optimizing essential parameters, such as batch size and nearest neighbor selection in sampling-based methods, can en…
Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics
Liding Zhang, Kuanqi Cai, Zhenshan Bing +2
Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective. This process relies on heuristic functions to guide the sea…
APT*: Asymptotically Optimal Motion Planning via Adaptively Prolated Elliptical R-Nearest Neighbors
Liding Zhang, Sicheng Wang, Kuanqi Cai +5
Optimal path planning aims to determine a sequence of states from a start to a goal while accounting for planning objectives. Popular methods often integrate fixed batch sizes and…
Tree-Based Grafting Approach for Bidirectional Motion Planning with Local Subsets Optimization
Liding Zhang, Yao Ling, Zhenshan Bing +3
Bidirectional motion planning often reduces planning time compared to its unidirectional counterparts. It requires connecting the forward and reverse search trees to form a continu…
Elliptical K-Nearest Neighbors -- Path Optimization via Coulomb's Law and Invalid Vertices in C-space Obstacles
Liding Zhang, Zhenshan Bing, Yu Zhang +5
Path planning has long been an important and active research area in robotics. To address challenges in high-dimensional motion planning, this study introduces the Force Direction…