112 citations · 304 across the 149 of their papers we have counts for
13 papers · 2 filters
Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow
Karthik Dharmarajan, Wenlong Huang, Jiajun Wu +2
Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to tra…
Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving
Haohong Lin, Yunzhi Zhang, Wenhao Ding +2
End-to-end (E2E) autonomous driving models have demonstrated strong performance in open-loop evaluations but often suffer from cascading errors and poor generalization in closed-lo…
10 Open Challenges Steering the Future of Vision-Language-Action Models
Soujanya Poria, Navonil Majumder, Chia-Yu Hung +7
Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly prevalent in the embodied AI arena, following the widespread succ…
TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System
Yanjie Ze, Siheng Zhao, Weizhuo Wang +6
Large-scale data has driven breakthroughs in robotics, from language models to vision-language-action models in bimanual manipulation. However, humanoid robotics lacks equally effe…
Retargeting Matters: General Motion Retargeting for Humanoid Motion Tracking
Joao Pedro Araujo, Yanjie Ze, Pei Xu +2
Humanoid motion tracking policies are central to building teleoperation pipelines and hierarchical controllers, yet they face a fundamental challenge: the embodiment gap between hu…
MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation
Chengshu Li, Mengdi Xu, Arpit Bahety +11
Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time-consuming. This ch…
Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Maggie Wang, Stephen Tian, Aiden Swann +3
Learning robotic manipulation policies directly in the real world can be expensive and time-consuming. While reinforcement learning (RL) policies trained in simulation present a sc…
DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation
Suzannah Wistreich, Baiyu Shi, Stephen Tian +5
Human skin provides a rich tactile sensing stream, localizing intentional and unintentional contact events over a large and contoured region. Replicating these tactile sensing capa…
VisualMimic: Visual Humanoid Loco-Manipulation via Motion Tracking and Generation
Shaofeng Yin, Yanjie Ze, Hong-Xing Yu +2
Humanoid loco-manipulation in unstructured environments demands tight integration of egocentric perception and whole-body control. However, existing approaches either depend on ext…
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation
Yihe Tang, Wenlong Huang, Yingke Wang +5
Understanding fine-grained object affordances is imperative for robots to manipulate objects in unstructured environments given open-ended task instructions. However, existing meth…
Learning Compositional Behaviors from Demonstration and Language
Weiyu Liu, Neil Nie, Ruohan Zhang +2
We introduce Behavior from Language and Demonstration (BLADE), a framework for long-horizon robotic manipulation by integrating imitation learning and model-based planning. BLADE l…
TWIST: Teleoperated Whole-Body Imitation System
Yanjie Ze, Zixuan Chen, João Pedro Araújo +4
Teleoperating humanoid robots in a whole-body manner marks a fundamental step toward developing general-purpose robotic intelligence, with human motion providing an ideal interface…
BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities
Yunfan Jiang, Ruohan Zhang, Josiah Wong +7
Real-world household tasks present significant challenges for mobile manipulation robots. An analysis of existing robotics benchmarks reveals that successful task performance hinge…