Year: 2023
Type: conference-paper
Abstract: In this paper, we present a novel method for achieving dexterous manipulation of complex objects, while simultaneously securing the object without the use of passive support surfaces.We posit that a key difficulty for training such policies in a Reinforcement Learning framework is the difficulty of exploring the problem state space, as the accessible regions of this space form a complex structure along manifolds of a high-dimensional space.To address this challenge, we use two versions of the non-holonomic Rapidly-Exploring Random Trees algorithm; one version is more general, but requires explicit use of the environment's transition function, while the second version uses manipulation-specific kinematic constraints to attain better sample efficiency.In both cases, we use states found via sampling-based exploration to generate reset distributions that enable training control policies under full dynamic constraints via model-free Reinforcement Learning.We show that these policies are effective at manipulation problems of higher difficulty than previously shown, and also transfer effectively to real robots.A number of example videos can also be found on the project website: sbrl.cs.
Source: Robotics: Science and Systems
Authors: Gagan Khandate, Siqi Shang, Eric Chang, Tristan Saidi, Johnson Adams, Matei Ciocarlie
Institution: Columbia University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4385403828