159 citations · 182 across the 22 of their papers we have counts for
26 papers
Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction
Weiye Zhao, Feihan Li, Yifan Sun +4
Enforcing state-wise safety constraints is critical for the application of reinforcement learning (RL) in real-world problems, such as autonomous driving and robot manipulation. Ho…
Robots that Learn to Safely Influence via Prediction-Informed Reach-Avoid Dynamic Games
Ravi Pandya, Changliu Liu, Andrea Bajcsy
Robots can influence people to accomplish their tasks more efficiently: autonomous cars can inch forward at an intersection to pass through, and tabletop manipulators can go for an…
Optimizing Multi-Touch Textile and Tactile Skin Sensing Through Circuit Parameter Estimation
Bo Ying Su, Yuchen Wu, Chengtao Wen +1
Tactile and textile skin technologies have become increasingly important for enhancing human-robot interaction and allowing robots to adapt to different environments. Despite notab…
Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning
Zheng Wu, Yichuan Li, Wei Zhan +3
The development of robotic systems for palletization in logistics scenarios is of paramount importance, addressing critical efficiency and precision demands in supply chain managem…
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
Tairan He, Zhengyi Luo, Wenli Xiao +4
We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB ca…
The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results
Christopher Brix, Stanley Bak, Changliu Liu +1
This report summarizes the 4th International Verification of Neural Networks Competition (VNN-COMP 2023), held as a part of the 6th Workshop on Formal Methods for ML-Enabled Autono…