4 papers
Beyond End-Task Success: How to Audit Visual Experience Retrieval in Robotics
Eshika Pathak, Leela Krishna
Robots that store past experiences must select which one to reuse in a new scene. Most systems select by visual similarity, and most evaluations report only the success of the sele…
When Should a Failing Robot Ask? Initiating Corrective Human-Robot Dialogue from Audited Sensor Evidence
Eshika Pathak, Leela Krishna
A robot that fails at a task faces the first decision in corrective dialogue: act on its own diagnosis, consult another onboard sensor, or interrupt a person. Choosing well require…
A Robust Task-Level Control Architecture for Learned Dynamical Systems
Eshika Pathak, Ahmed Aboudonia, Sandeep Banik +1
Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation ('task') space of robotic systems. However, realizing…
Natural Policy Gradient for Average Reward Non-Stationary RL
Neharika Jali, Eshika Pathak, Pranay Sharma +2
We consider the problem of non-stationary reinforcement learning (RL) in the infinite-horizon average-reward setting. We model it by a Markov Decision Process with time-varying rew…