TY - GEN
T1 - Combining neural networks and tree search for task and motion planning in challenging environments
AU - Paxton, Chris
AU - Raman, Vasumathi
AU - Hager, Gregory D.
AU - Kobilarov, Marin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - Task and motion planning subject to Linear Temporal Logic (LTL) specifications in complex, dynamic environments requires efficient exploration of many possible future worlds. Model-free reinforcement learning has proven successful in a number of challenging tasks, but shows poor performance on tasks that require long-term planning. In this work, we integrate Monte Carlo Tree Search with hierarchical neural net policies trained on expressive LTL specifications. We use reinforcement learning to find deep neural networks representing both low-level control policies and task-level 'option policies' that achieve high-level goals. Our combined architecture generates safe and responsive motion plans that respect the LTL constraints. We demonstrate our approach in a simulated autonomous driving setting, where a vehicle must drive down a road in traffic, avoid collisions, and navigate an intersection, all while obeying rules of the road.
AB - Task and motion planning subject to Linear Temporal Logic (LTL) specifications in complex, dynamic environments requires efficient exploration of many possible future worlds. Model-free reinforcement learning has proven successful in a number of challenging tasks, but shows poor performance on tasks that require long-term planning. In this work, we integrate Monte Carlo Tree Search with hierarchical neural net policies trained on expressive LTL specifications. We use reinforcement learning to find deep neural networks representing both low-level control policies and task-level 'option policies' that achieve high-level goals. Our combined architecture generates safe and responsive motion plans that respect the LTL constraints. We demonstrate our approach in a simulated autonomous driving setting, where a vehicle must drive down a road in traffic, avoid collisions, and navigate an intersection, all while obeying rules of the road.
UR - http://www.scopus.com/inward/record.url?scp=85041951435&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041951435&partnerID=8YFLogxK
U2 - 10.1109/IROS.2017.8206505
DO - 10.1109/IROS.2017.8206505
M3 - Conference contribution
AN - SCOPUS:85041951435
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6059
EP - 6066
BT - IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Y2 - 24 September 2017 through 28 September 2017
ER -