6 papers
Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors
Zixing Wang, Kausik Sivakumar, Jinghuan Shang +5
Bridging the sim-to-real gap is a core challenge in deploying learned manipulation policies. Sim-to-real learning is attractive because it can replace expensive real robot demonstr…
When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning
Lakshita Dodeja, Ondrej Biza, Shivam Vats +5
Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning. However, BC lacks a self-guided mechanism for online improvement after demonstrations have been…
You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector
Omkar Patil, Ondrej Biza, Thomas Weng +9
What happens when a pretrained generative robot policy is provided a constant initial noise as input, rather than repeatedly sampling it from a Gaussian? We demonstrate that the pe…
Scaling Short-Term Memory of Visuomotor Policies for Long-Horizon Tasks
Rutav Shah, Rajat Kumar Jenamani, Xiaohan Zhang +5
Many robotic tasks require short-term memory, whether it's retrieving an object that's no longer visible or turning off an appliance after a set period. Yet, most visuomotor polici…
ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors
Zifan Xu, Ran Gong, Maria Vittoria Minniti +10
Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (e.g., through teleoperation) serve as…
SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning
Philip Schroeder, Thomas Weng, Karl Schmeckpeper +3
Vision-language models (VLMs) have shown impressive capabilities across diverse tasks, motivating efforts to leverage these models to supervise robot learning. However, when used a…