Efficiency and abstraction in task and motion planning

Modern robots are capable of complex and highly dynamic behaviors, yet the decisionmaking algorithms that drive them struggle to solve problems involving complex behaviors like manipulation. The combination of continuous and discrete dynamics induced by contact creates severe computational challenges, and most known practical approaches rely on hand-designed discrete representations to mitigate computational issues. However, the relationship between the discrete representation and the physical robot is poorly understood and cannot easily be empirically verified, and so many planning systems are brittle and prone to failure when the robot encounters situations not anticipated by the model designer. This thesis addresses the limitations of conventional representations for task and motion planning by introducing a constraint-based representation that explicitly places continuous and discrete dynamics on equal footing.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Vega-Brown, Will(William Robert)

Advisor dc:contributor.advisor

• Nicholas Roy.

• Mechanical Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127732

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127732