5 citations · 6 across the 5 of their papers we have counts for
5 papers
Robot Learning with Super-Linear Scaling
Marcel Torne, Arhan Jain, Jiayi Yuan +5
Scaling robot learning requires data collection pipelines that scale favorably with human effort. In this work, we propose Crowdsourcing and Amortizing Human Effort for Real-to-Sim…
From Imitation to Refinement -- Residual RL for Precise Assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld +2
Recent advances in Behavior Cloning (BC) have made it easy to teach robots new tasks. However, we find that the ease of teaching comes at the cost of unreliable performance that sa…
Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
Marcel Torne, Anthony Simeonov, Zechu Li +4
Imitation learning methods need significant human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement lear…
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback
Max Balsells, Marcel Torne, Zihan Wang +3
Ideally, we would place a robot in a real-world environment and leave it there improving on its own by gathering more experience autonomously. However, algorithms for autonomous ro…
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
Marcel Torne, Max Balsells, Zihan Wang +4
Exploration and reward specification are fundamental and intertwined challenges for reinforcement learning. Solving sequential decision-making tasks requiring expansive exploration…