5 citations · 5 across the 9 of their papers we have counts for
9 papers
DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation
Yu Fang, Wanxi Dong, Jiaqi Liu +7
Reinforcement learning holds great promise for improving robot policies beyond the limits of imitation learning. However, its practical adoption remains bottlenecked by the lack of…
HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
Kevin Lin, Ajay Mandlekar, Caelan Reed Garrett +7
Imitation learning is a promising approach for training humanoid robots to both walk and manipulate, but it requires a large number of demonstrations, which are time-intensive and…
When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs
Yu Fang, Yuchun Feng, Dong Jing +5
Vision-Language-Action models (VLAs) promise to ground language instructions in robot control, yet in practice often fail to faithfully follow language. When presented with instruc…
INHerit-SG: Incremental Hierarchical Semantic Scene Graphs for Complex Embodied Retrieval
YukTungSamuel Fang, Zhikang Shi, Jiabin Qiu +5
Object retrieval in 3D scenes remains difficult when queries combine hierarchical context, attributes, negation, and chained spatial relations. Existing semantic maps, scene graphs…
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
Joel Jang, Seonghyeon Ye, Zongyu Lin +25
We introduce DreamGen, a simple yet highly effective 4-stage pipeline for training robot policies that generalize across behaviors and environments through neural trajectories - sy…
FLARE: Robot Learning with Implicit World Modeling
Ruijie Zheng, Jing Wang, Scott Reed +18
We introduce uture tent presentation Alignment (), a novel framework that integrates predictive latent world modeling into rob…
Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation
Abhiram Maddukuri, Zhenyu Jiang, Lawrence Yunliang Chen +12
Large real-world robot datasets hold great potential to train generalist robot models, but scaling real-world human data collection is time-consuming and resource-intensive. Simula…
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
NVIDIA, :, Johan Bjorck +40
General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist…
ReBot: Scaling Robot Learning with Real-to-Sim-to-Real Robotic Video Synthesis
Yu Fang, Yue Yang, Xinghao Zhu +4
Vision-language-action (VLA) models present a promising paradigm by training policies directly on real robot datasets like Open X-Embodiment. However, the high cost of real-world d…