Hao-Tien Lewis Chiang
I'm a PhD Student Researcher from the University of New Mexico. My research interest is in integrating traditional robotics techniques with state of the art machine learning.
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Fast Deep Swept Volume Estimator
John E. G. Baxter
Satomi Sugaya
Mohammad R. Yousefi
Lydia Tapia
The International Journal of Robotics Research (IJRR) (2020) (to appear)
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Despite decades of research on efficient swept volume computation for robotics, computing the exact swept volume is intractable and approximate swept volume algorithms have been computationally prohibitive for applications such as motion and task planning. In this work, we employ Deep Neural Networks (DNNs) for fast swept volume estimation. Since swept volume is a property of robot kinematics, a DNN can be trained off-line once in a supervised manner and deployed in any environment. The trained DNN is fast during on-line swept volume geometry or size inferences. Results show that DNNs can accurately and rapidly estimate swept volumes caused by rotational, translational and prismatic joint motions. Sampling-based planners using the learned distance are up to 5x more efficient and identify paths with smaller swept volumes on simulated and physical robots. Results also show that swept volume geometry estimation with a DNN is over 98.9% accurate and 1200x faster than an octree-based swept volume algorithm.
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Long-Range Indoor Navigation with PRM-RL
Anthony Francis
Marek Fiser
Tsang-Wei Lee
IEEE Transactions on Robotics (T-RO) (2020), pp. 19
Learning Navigation Behaviors End-to-End with AutoRL
Marek Fiser
Anthony Francis
IEEE Robotics and Automation Letters (RA-L), 4 (2019), pp. 2007-2014
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We learn end-to-end point-to-point and path-following navigation behaviors that avoid moving obstacles. These policies receive noisy lidar observations and output robot linear and angular velocities. The policies are trained in small, static environments with AutoRL, an evolutionary automation layer around Reinforcement Learning (RL) that searches for a deep RL reward and neural network architecture with large-scale hyper-parameter optimization. AutoRL first finds a reward that maximizes task completion, and then finds a neural network architecture that maximizes the cumulative of the found reward. Empirical evaluations, both in simulation and on-robot, show that AutoRL policies do not suffer from the catastrophic forgetfulness that plagues many other deep reinforcement learning algorithms, generalize to new environments and moving obstacles, are robust to sensor, actuator, and localization noise, and can serve as robust building blocks for larger navigation tasks. Our path-following and point-to-point policies are respectively 23% and 26% more successful than comparison methods across new environments. Video at: https://youtu.be/0UwkjpUEcbI
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Deep Reinforcement Learning (RL) has recently emerged as a solution for moving obstacle avoidance. Deep RL learns to simultaneously predict obstacle motions and corresponding avoidance actions directly from robot sensors, even for obstacles with different dynamics models. However, deep RL methods typically cannot guarantee policy convergences, i.e., cannot provide probabilistic collision avoidance guarantees. In contrast, stochastic reachability (SR), a computationally expensive formal method that employs a known obstacle dynamics model, identifies the optimal avoidance policy and provides strict convergence guarantees. The availability of the optimal solution for versions of the moving obstacle problem provides a baseline to compare trained deep RL policies. In this paper, we compare the expected cumulative reward and actions of these policies to SR, and find the following. 1) The state-value function approximates the optimal collision probability well, thus explaining the high empirical performance. 2) RL policies deviate from the optimal significantly thus negatively impacting collision avoidance in some cases. 3) Evidence suggests that the deviation is caused, at least partially, by the actor net failing to approximate the action corresponding to the highest state-action value.
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RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies
Marek Fiser
Lydia Tapia
IEEE Robotics and Automation Letters (RA-L) (2019)
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This paper addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning. First, we use deep reinforcement learning to learn an obstacle-avoiding policy that maps a robot's sensor observations to actions, which is used as a local planner during planning and as a controller during execution. Second, we train a reachability estimator in a supervised manner, which predicts the RL policy's time to reach a state in the presence of obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local planner, and the reachability estimator as the distance function to bias tree-growth towards promising regions. We evaluate our method on three kinodynamic systems, including physical robot experiments. Results across all three robots tested indicate that RL-RRT outperforms state of the art kinodynamic planners in efficiency, and also provides a shorter path finish time than a steering function free method. The learned local planner policy and accompanying reachability estimator demonstrate transferability to the previously unseen experimental environments, making RL-RRT fast because the expensive computations are replaced with simple neural network inference. Video: https://youtu.be/dDMVMTOI8KY
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Fast Swept Volume Estimation with Deep Learning
Satomi Sugaya
Lydia Tapia
The 13th International Workshop on the Algorithmic Foundations of Robotics (WAFR) (2018)
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Swept volume, the volume displaced by a moving object, is an ideal distance metric for sampling-based motion planning because it directly correlates to the amount of motion between two states. However, even approximate algorithms are computationally prohibitive. Our fundamental approach is the application of deep learning to efficiently estimate swept volume computation within a 5%-10% error for all robots tested, from rigid bodies to manipulators. However, even inference via the trained network can be computationally costly given the often hundreds of thousands of computations required by sampling-based motion planning. To address this, we demonstrate an efficient hierarchical approach for applying our trained estimator. This approach first pre-filters samples using a weighted Euclidean estimator trained via swept volume. Then, it selectively applies the deep neural network estimator. The first estimator, although less accurate, has metric space properties. The second estimator is a high-fidelity unbiased estimator without metric space properties. We integrate the hierarchical selection approach in both roadmap-based and a tree-based sampling motion planners. Empirical evaluation on the robot set demonstrates that hierarchal application of the metrics yields up to 5000 times faster planning than state of the art swept volume approximation and up to five times higher probability of finding a collision-free trajectory under a fixed time budget than the traditional Euclidean metric.
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