1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.RO2023★ 1 cited
Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning
Chengmin Zhou, Xin Lu, Jiapeng Dai +3
Motion planning is the soul of robot decision making. Classical planning algorithms like graph search and reaction-based algorithms face challenges in cases of dense and dynamic ob…
cs.AI2023★ 1 cited
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
Chengmin Zhou, Chao Wang, Haseeb Hassan +3
Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees o…