6 papers
Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling
Yuanchu Liang, Edward Kim, J. Arden Knoll +4
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalabil…
Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
Benedict Quartey, Sebastian Castro, Eric Rosen +3
Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to new compositions of known skil…
AORRTC: Almost-Surely Asymptotically Optimal Planning with RRT-Connect
Tyler Wilson, Wil Thomason, Zachary Kingston +1
Finding high-quality solutions quickly is an important objective in motion planning. This is especially true for high-degree-of-freedom robots. Satisficing planners have traditiona…
Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)
Tyler S. Wilson, Wil Thomason, Zachary Kingston +2
Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. T…
Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling
Yuanchu Liang, Edward Kim, Wil Thomason +3
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalabil…
Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for Fast Collision Checking
Clayton W. Ramsey, Zachary Kingston, Wil Thomason +1
Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems…