163 citations · 589 across the 42 of their papers we have counts for
10 papers · 1 filter
Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa +1
We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial…
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
Vivswan Shitole, Li Fuxin, Minsuk Kahng +2
Attention maps are a popular way of explaining the decisions of convolutional networks for image classification. Typically, for each image of interest, a single attention map is pr…
Learning Task Space Actions for Bipedal Locomotion
Helei Duan, Jeremy Dao, Kevin Green +3
Recent work has demonstrated the success of reinforcement learning (RL) for training bipedal locomotion policies for real robots. This prior work, however, has focused on learning…
Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition
Jonah Siekmann, Yesh Godse, Alan Fern +1
We study the problem of realizing the full spectrum of bipedal locomotion on a real robot with sim-to-real reinforcement learning (RL). A key challenge of learning legged locomotio…
Dream and Search to Control: Latent Space Planning for Continuous Control
Anurag Koul, Varun V. Kumar, Alan Fern +1
Learning and planning with latent space dynamics has been shown to be useful for sample efficiency in model-based reinforcement learning (MBRL) for discrete and continuous control…
Learning Spring Mass Locomotion: Guiding Policies with a Reduced-Order Model
Kevin Green, Yesh Godse, Jeremy Dao +3
In this paper, we describe an approach to achieve dynamic legged locomotion on physical robots which combines existing methods for control with reinforcement learning. Specifically…