53 citations · 227 across the 20 of their papers we have counts for
7 papers · 1 filter
RMPflow: A Computational Graph for Automatic Motion Policy Generation
Ching-An Cheng, Mustafa Mukadam, Jan Issac +4
We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs). RMPs are a class of reactive motion…
Truncated Back-propagation for Bilevel Optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch +1
Bilevel optimization has been recently revisited for designing and analyzing algorithms in hyperparameter tuning and meta learning tasks. However, due to its nested structure, eval…
Predictor-Corrector Policy Optimization
Ching-An Cheng, Xinyan Yan, Nathan Ratliff +1
We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that…
Orthogonally Decoupled Variational Gaussian Processes
Hugh Salimbeni, Ching-An Cheng, Byron Boots +1
Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its superlinear computational and memory complexities h…
Accelerating Imitation Learning with Predictive Models
Ching-An Cheng, Xinyan Yan, Evangelos A. Theodorou +1
Sample efficiency is critical in solving real-world reinforcement learning problems, where agent-environment interactions can be costly. Imitation learning from expert advice has p…
Fast Policy Learning through Imitation and Reinforcement
Ching-An Cheng, Xinyan Yan, Nolan Wagener +1
Imitation learning (IL) consists of a set of tools that leverage expert demonstrations to quickly learn policies. However, if the expert is suboptimal, IL can yield policies with i…
Convergence of Value Aggregation for Imitation Learning
Ching-An Cheng, Byron Boots
Value aggregation is a general framework for solving imitation learning problems. Based on the idea of data aggregation, it generates a policy sequence by iteratively interleaving…