12 citations · 23 across the 7 of their papers we have counts for
7 papers
Robot Parkour Learning
Ziwen Zhuang, Zipeng Fu, Jianren Wang +4
Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse…
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills
Zenan Li, Fan Nie, Qiao Sun +2
Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets. While offline reinforcement lea…
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation
Renhao Wang, Jiayuan Mao, Joy Hsu +3
Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills. Towards this goal, recent w…
Neural Map Prior for Autonomous Driving
Xuan Xiong, Yicheng Liu, Tianyuan Yuan +3
High-definition (HD) semantic maps are crucial in enabling autonomous vehicles to navigate urban environments. The traditional method of creating offline HD maps involves labor-int…
SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer
Xuanyao Chen, Zhijian Liu, Haotian Tang +3
High-resolution images enable neural networks to learn richer visual representations. However, this improved performance comes at the cost of growing computational complexity, hind…
VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction
Xin Huang, Xiaoyu Tian, Junru Gu +2
Predicting future behaviors of road agents is a key task in autonomous driving. While existing models have demonstrated great success in predicting marginal agent future behaviors,…