32 citations · 108 across the 28 of their papers we have counts for
5 papers · 1 filter
Imagining Goals with Geometric Horizon Models for Policy Generalization
Daniel Lawson, Dhruv Sreenivas, Adriana Hugessen +2
Training from offline data has allowed for substantial progress in domains such as robotics, leading to general-purpose policies that can be easily applied zero-shot or efficiently…
Difference-Aware Retrieval Policies for Imitation Learning
Quinn Pfeifer, Ethan Pronovost, Paarth Shah +3
Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment. We show that reusi…
Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments
Michael Beukman, Khimya Khetarpal, Zeyu Zheng +4
An agent's performance stagnating at a suboptimal level is a common problem in deep on-policy RL. Focusing on PPO, we show that plateaus in certain regimes arise not because of kno…
Affordances Enable Partial World Modeling with LLMs
Khimya Khetarpal, Gheorghe Comanici, Jonathan Richens +5
Full models of the world require complex knowledge of immense detail. While pre-trained large models have been hypothesized to contain similar knowledge due to extensive pre-traini…
The Geometry of Learning to Avoid Interventions
Ethan Pronovost, Khimya Khetarpal, Siddhartha Srinivasa
Human interventions are a common source of supervision in autonomous systems during deployment. Many existing approaches are based on avoiding interventions, yet the consequences o…