3 citations · 4 across the 5 of their papers we have counts for
5 papers
Practice Makes Perfect: Planning to Learn Skill Parameter Policies
Nishanth Kumar, Tom Silver, Willie McClinton +5
One promising approach towards effective robot decision making in complex, long-horizon tasks is to sequence together parameterized skills. We consider a setting where a robot is i…
Steerable Equivariant Representation Learning
Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang +4
Pre-trained deep image representations are useful for post-training tasks such as classification through transfer learning, image retrieval, and object detection. Data augmentation…
Learning Efficient Abstract Planning Models that Choose What to Predict
Nishanth Kumar, Willie McClinton, Rohan Chitnis +3
An effective approach to solving long-horizon tasks in robotics domains with continuous state and action spaces is bilevel planning, wherein a high-level search over an abstraction…
Predicate Invention for Bilevel Planning
Tom Silver, Rohan Chitnis, Nishanth Kumar +4
Efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to…
HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning
Willie McClinton, Andrew Levy, George Konidaris
Sparse rewards and long time horizons remain challenging for reinforcement learning algorithms. Exploration bonuses can help in sparse reward settings by encouraging agents to expl…