354 citations · 1.1k across the 115 of their papers we have counts for
6 papers · 1 filter
X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets
Prithwish Dan, Chenyang Ma, Wei Zhan
Reinforcement learning (RL) in simulation can train dexterous manipulation policies without robot demonstrations, but training a single generalist policy with task-agnostic rewards…
AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion
Xirui Liang, Jiaqi Liang, Jingkai Xu +6
Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback, a capability that remains challenging to replicate in robotic systems. E…
LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving
Hao Shao, Letian Wang, Yang Zhou +5
Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck for large-scale deployment. To…
RAYNOVA: Scale-Temporal Autoregressive World Modeling in Ray Space
Yichen Xie, Chensheng Peng, Mazen Abdelfattah +6
World foundation models aim to simulate the evolution of the real world with physically plausible behavior. Unlike prior methods that handle spatial and temporal correlations separ…
HetroD: A High-Fidelity Drone Dataset and Benchmark for Autonomous Driving in Heterogeneous Traffic
Yu-Hsiang Chen, Wei-Jer Chang, Christian Kotulla +7
We present HetroD, a dataset and benchmark for developing autonomous driving systems in heterogeneous environments. HetroD targets the critical challenge of navi- gating real-world…
NoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning
Ishaan Rawal, Shubh Gupta, Yihan Hu +1
Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures. However, current VLAs face two expensive…