Wednesday, November 3, 2021 - 1:00pm to 2:00pm
Zoom: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5UT09

Speaker Information

Caelan Garrett
Research Scientist
NVIDIA

Abstract

We seek to program a robot to autonomously complete complex tasks in a variety of real-world settings involving different environments, objects, manipulation skills, degrees of observability, initial states, and goal objectives. In order to successfully generalize across these settings, we take a model-based approach to building the robot's policy, which enables it to reason about the effects of it executing different sequences of parameterized manipulation skills. Specifically, we introduce a general-purpose hybrid planning framework that uses streams, modules that encode sampling procedures, to generate continuous parameter-value candidates. We present several domain-independent algorithms that efficiently combine streams in order to solve for parameter values that jointly satisfy the constraints necessary for a sequence of skills to achieve the goal. Each stream can be either engineered to perform a standard robotics subroutine, like inverse kinematics and collision checking, or learned from data to capture difficult-to-model behaviors, such as pouring, scooping, and grasping. Streams are also able to represent probabilistic inference operations, which enables our framework to plan in belief space and intentionally select actions that reduce the robot's uncertainty about the unknown world. We demonstrate the generality of our approach by applying it to several real-world tabletop, kitchen, and construction tasks and show that it can even be effective in settings involving objects that the robot has never seen before.

Speaker Bio

Caelan Garrett is a research scientist at NVIDIA's Seattle Robotics Lab which is led by Professor Dieter Fox. He received his PhD at MIT in the Learning and Intelligent Systems group within CSAIL where he was advised by Professors Tomás Lozano-Pérez and Leslie Pack Kaelbling. His research is on integrating robot motion planning, discrete AI planning, and machine learning to flexibly and efficiently plan for autonomous mobile manipulators operating in human environments. He recently authored the first survey paper on integrated task and motion planning. He is a recipient of the NSF Graduate Research Fellowship. He has previously interned in the autonomous vehicle industry while at Optimus Ride and in the autonomous fulfillment industry while at Amazon Robotics.