19 citations · 27 across the 6 of their papers we have counts for
6 papers
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations
Albert Wilcox, Ashwin Balakrishna, Jules Dedieu +3
Providing densely shaped reward functions for RL algorithms is often exceedingly challenging, motivating the development of RL algorithms that can learn from easier-to-specify spar…
Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features
Justin Kerr, Huang Huang, Albert Wilcox +4
Humans make extensive use of vision and touch as complementary senses, with vision providing global information about the scene and touch measuring local information during manipul…
Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles
Albert Wilcox, Justin Kerr, Brijen Thananjeyan +5
Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks s…
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
Ryan Hoque, Ashwin Balakrishna, Ellen Novoseller +3
Effective robot learning often requires online human feedback and interventions that can cost significant human time, giving rise to the central challenge in interactive imitation…
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks
Albert Wilcox, Ashwin Balakrishna, Brijen Thananjeyan +2
Reinforcement learning (RL) has shown impressive success in exploring high-dimensional environments to learn complex tasks, but can often exhibit unsafe behaviors and require exten…
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Nathan O. Lambert, Albert Wilcox, Howard Zhang +2
Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-st…