Planning Robust Strategies for Constructing Multi-object Arrangements
Name
MIT-CSAIL-TR-2017-007.pdf
Size
4.31 MB
Format
Adobe PDF
Checksum (MD5)
6b8cb285d81027d354959d20583715c2
Download all files submitted through automated deposit
Author(s)
Anders, Ariel
Kaelbling, Leslie
Lozano-Perez, Tomas
Advisor(s)
Tomas Lozano-Perez
Date Issued
January 30, 2017
Series/Report no.
MIT-CSAIL-TR-2017-007
Abstract
A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. A prominent strategy for dealing with uncertainty is to construct a feedback policy, where actions are chosen as a function of the current state estimate. However, constructing such policies is computationally very difficult. An alternative strategy is conformant planning which finds open-loop action sequences that achieve the goal for all input states and action outcomes. In this work, we investigate the conformant planning approach to robot manipulation. In particular, we tackle the problem of pushing multiple objects simultaneously to achieve a specified arrangement. Conformant planning is a belief-state planning problem. A belief state is the set of all possible states of the world, and the goal is to find a sequence of actions that will bring an initial belief state to a goal belief state To do forward belief-state planning, we created a deterministic belief-state transition model from supervised learning based on physics simulations. A key pitfall in conformant planning is that the complexity of the belief state tends to increase with each operation, making it increasingly harder to compute the effect of actions. This work explores the idea that we can construct conformant plans for robot manipulation by only using actions resulting in compact belief states.
Subjects
manipulation
robotics
machine learning
belief space
planning
uncertainty
Persistent DSpace Link
