Monday, June 10
3:00 pm to 4:00 pm
3305 Newell-Simon Hall
3:00 pm to 4:00 pm
3305 Newell-Simon Hall
Reinforcement Learning with Spatial Reasoning for Dexterous Robotic Manipulation
Abstract:
Robotic manipulation in unstructured environments requires adaptability and the ability to handle a wide variety of objects and tasks. This thesis presents novel approaches for learning robotic manipulation skills using reinforcement learning (RL) with spatially-grounded action spaces, addressing the challenges of high-dimensional, continuous action spaces and alleviating the need for extensive training data.
Robotic manipulation in unstructured environments requires adaptability and the ability to handle a wide variety of objects and tasks. This thesis presents novel approaches for learning robotic manipulation skills using reinforcement learning (RL) with spatially-grounded action spaces, addressing the challenges of high-dimensional, continuous action spaces and alleviating the need for extensive training data.
Our first contribution, HACMan (Hybrid Actor-Critic Maps for Manipulation), introduces a hybrid actor-critic model that maps discrete and continuous actions to 3D object point clouds, enabling complex non-prehensile interactions based on the spatial features of the object. Our second contribution, HACMan++ (Spatially-Grounded Motion Primitives for Manipulation), extends the framework to more generalized manipulation. It includes a diverse set of parameterized motion primitives, allowing the robot to perform a wide range of tasks by chaining these primitives together.
Committee:
Prof. David Held (chair)
Prof. Changliu Liu
Xianyi Cheng