5 papers
Learned Critical Probabilistic Roadmaps for Robotic Motion Planning
Brian Ichter, Edward Schmerling, Tsang-Wei Edward Lee +1
Sampling-based motion planning techniques have emerged as an efficient algorithmic paradigm for solving complex motion planning problems. These approaches use a set of probing samp…
Neural Collision Clearance Estimator for Batched Motion Planning
J. Chase Kew, Brian Ichter, Maryam Bandari +2
We present a neural network collision checking heuristic, ClearanceNet, and a planning algorithm, CN-RRT. ClearanceNet learns to predict separation distance (minimum distance betwe…
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation
Xinlei Pan, Tingnan Zhang, Brian Ichter +3
Imitation learning is a popular approach for training visual navigation policies. However, collecting expert demonstrations for legged robots is challenging as these robots can be…
Group Marching Tree: Sampling-Based Approximately Optimal Motion Planning on GPUs
Brian Ichter, Edward Schmerling, Marco Pavone
This paper presents a novel approach, named the Group Marching Tree (GMT*) algorithm, to planning on GPUs at rates amenable to application within control loops, allowing planning i…
Perception-Aware Motion Planning via Multiobjective Search on GPUs
Brian Ichter, Benoit Landry, Edward Schmerling +1
In this paper we describe a framework towards computing well-localized, robust motion plans through the perception-aware motion planning problem, whereby we seek a low-cost motion…