10 papers
Benchmarking Population-Based Reinforcement Learning across Robotic Tasks with GPU-Accelerated Simulation
Asad Ali Shahid, Yashraj Narang, Vincenzo Petrone +5
In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks. However, this comes at the cost of an enormous amount of…
The Reality Gap in Robotics: Challenges, Solutions, and Best Practices
Elie Aljalbout, Jiaxu Xing, Angel Romero +9
Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driv…
VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning
Binghao Huang, Jie Xu, Iretiayo Akinola +8
Humans excel at bimanual assembly tasks by adapting to rich tactile feedback -- a capability that remains difficult to replicate in robots through behavioral cloning alone, due to…
Refinery: Active Fine-tuning and Deployment-time Optimization for Contact-Rich Policies
Bingjie Tang, Iretiayo Akinola, Jie Xu +6
Simulation-based learning has enabled policies for precise, contact-rich tasks (e.g., robotic assembly) to reach high success rates (~80%) under high levels of observation noise an…
Neural Robot Dynamics
Jie Xu, Eric Heiden, Iretiayo Akinola +3
Accurate and efficient simulation of modern robots remains challenging due to their high degrees of freedom and intricate mechanisms. Neural simulators have emerged as a promising…
RobotSmith: Generative Robotic Tool Design for Acquisition of Complex Manipulation Skills
Chunru Lin, Haotian Yuan, Yian Wang +7
Endowing robots with tool design abilities is critical for enabling them to solve complex manipulation tasks that would otherwise be intractable. While recent generative frameworks…