31 citations · 95 across the 18 of their papers we have counts for
6 papers · 1 filter
Learning Representations that Enable Generalization in Assistive Tasks
Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown +2
Recent work in sim2real has successfully enabled robots to act in physical environments by training in simulation with a diverse ''population'' of environments (i.e. domain randomi…
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…
The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types
Gaurav R. Ghosal, Matthew Zurek, Daniel S. Brown +1
When inferring reward functions from human behavior (be it demonstrations, comparisons, physical corrections, or e-stops), it has proven useful to model the human as making noisy-r…
Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies
Satvik Sharma, Ellen Novoseller, Vainavi Viswanath +6
Simulation-to-reality transfer has emerged as a popular and highly successful method to train robotic control policies for a wide variety of tasks. However, it is often challenging…
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning
Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson +2
Learning policies via preference-based reward learning is an increasingly popular method for customizing agent behavior, but has been shown anecdotally to be prone to spurious corr…
Teaching Robots to Span the Space of Functional Expressive Motion
Arjun Sripathy, Andreea Bobu, Zhongyu Li +3
Our goal is to enable robots to perform functional tasks in emotive ways, be it in response to their users' emotional states, or expressive of their confidence levels. Prior work h…