5 accepted papers
Position controllers have become the dominant interface for executing learned manipulation policies. Yet a critical design decision remains understudied: how should we choose controller gains for policy learning? The conventional wisdom is to select gains based on desired task compliance or stiffnes…
The scarcity of diverse and high-quality data impedes the quest to build a generalist robotic system. Current robotics data collection efforts face many challenges: the need for physical robotic hardware, setting up the environment, frequent resets, and the fatigue for data collectors operating real
Many roboticists dream of presenting a robot with a task in the evening and returning the next morning to find the robot capable of solving the task. What is preventing us from achieving this? Sim-to-real reinforcement learning (RL) has achieved impressive performance on challenging robotics tasks,…
Programming manipulation behaviors can become increasingly difficult with a growing number and complexity of manipulation tasks, particularly in a dynamic and unstructured environment. Recent progress in unsupervised skill discovery algorithms has shown great promise in learning an extensive collect…
Robotic painting tasks in the real world are often made complicated by the highly complex and stochastic nature of the dynamics that underlie, e.g., physical contact between the painting tool and a canvas, color blendings between painting mediums, and many more. Simulation-based inverse graphics alg…