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Brian Hou

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

ICRA Conference 2024 Conference Paper

Multi-Sample Long Range Path Planning under Sensing Uncertainty for Off-Road Autonomous Driving

Matt Schmittle
Rohan Baijal
Brian Hou
Siddhartha S. Srinivasa
Byron Boots

We focus on the problem of long-range dynamic replanning for off-road autonomous vehicles, where a robot plans paths through a previously unobserved environment while continuously receiving noisy local observations. An effective approach for planning under sensing uncertainty is determinization, where one converts a stochastic world into a deterministic one and plans under this simplification. This makes the planning problem tractable, but the cost of following the planned path in the real world may be different than in the determinized world. This causes collisions if the determinized world optimistically ignores obstacles, or causes unnecessarily long routes if the determinized world pessimistically imagines more obstacles. We aim to be robust to uncertainty over potential worlds while still achieving the efficiency benefits of determinization. We evaluate algorithms for dynamic replanning on a large real-world dataset of challenging long-range planning problems from the DARPA RACER program. Our method, Dynamic Replanning via Evaluating and Aggregating Multiple Samples (DREAMS), outperforms other determinization-based approaches in terms of combined traversal time and collision cost. https://sites.google.com/cs.washington.edu/dreams/

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ICRA Conference 2023 Conference Paper

GuILD: Guided Incremental Local Densification for Accelerated Sampling-based Motion Planning

Rosario Scalise
Aditya Mandalika
Brian Hou
Sanjiban Choudhury
Siddhartha S. Srinivasa

Sampling-based motion planners rely on incre-mental densification to discover progressively shorter paths. After computing feasible path $\xi$ between start $x_{s}$ and goal $x_{t}$, the Informed Set (IS) prunes the configuration space $\mathcal{X}$ by conservatively eliminating points that cannot yield shorter paths. Densification via sampling from this Informed Set retains asymptotic optimality of sampling from the entire configuration space. For path length $c(\xi)$ and Euclidean heuristic $h, IS= \{x\vert x\in \mathcal{X}, \ h(x_{s}, \ x)+h(x, \ x_{t})\leq c(\xi)\}$. Relying on the heuristic can render the IS especially conservative in high dimensions or complex environments. Furthermore, the IS only shrinks when shorter paths are discovered. Thus, the computational effort from each iteration of densification and planning is wasted if it fails to yield a shorter path, despite improving the cost-to-come for vertices in the search tree. Our key insight is that even in such a failure, shorter paths to vertices in the search tree (rather than just the goal) can immediately improve the planner's sampling strategy. Guided Incremental Local Densification (GuILD) leverages this information to sample from Local Subsets of the IS. We show that GuILD significantly outperforms uniform sampling of the Informed Set in simulated $\mathbb{R}^{2}, SE(2)$ environments and manipulation tasks in $\mathbb{R}^{7}$.

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IROS Conference 2022 Conference Paper

Dynamic Replanning with Posterior Sampling

Brian Hou
Siddhartha S. Srinivasa

When navigating to a goal in an uncertain environment, a robot must simultaneously navigate the exploration-exploitation tradeoff: should it aim to gain information and reduce uncertainty, or should it simply brave the unknown? We formalize this as the Bayesian dynamic motion planning problem, and we analyze how several strategies from the literature balance these concerns via determinization and planning. Within the framework of determinization in the face of uncertainty, we shift the burden of exploration to determinization rather than planning. Dynamic Replanning with Posterior Sampling (DRPS) is very efficient: each iteration consists of a single posterior update and a shortest path query. Relative to comparative baselines across seven datasets of 2D planning problems, DRPS has a higher percentage of success, traverses lower or comparable total distances, and accelerates total planning time by 4–7×. Across a dataset of larger 7D Baxter manipulator planning problems, DRPS reduces total distance by 40% and total planning time by 18×.

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ICRA Conference 2022 Conference Paper

Stein Variational Probabilistic Roadmaps

Alexander Lambert
Brian Hou
Rosario Scalise
Siddhartha S. Srinivasa
Byron Boots

Efficient and reliable generation of global path plans are necessary for safe execution and deployment of autonomous systems. In order to generate planning graphs which adequately resolve the topology of a given environment, many sampling-based motion planners resort to coarse, heuristically-driven strategies which often fail to generalize to new and varied surroundings. Further, many of these approaches are not designed to contend with partial-observability. We posit that such uncertainty in environment geometry can, in fact, help drive the sampling process in generating feasible, and probabilistically-safe planning graphs. We propose a method for Probabilistic Roadmaps which relies on particle-based Variational Inference to efficiently cover the posterior distribution over feasible regions in configuration space. Our approach, Stein Variational Probabilistic Roadmap (SV-PRM), results in sample-efficient generation of planning-graphs and large improvements over traditional sampling approaches. We demonstrate the approach on a variety of challenging planning problems, including real-world probabilistic occupancy maps and high-dof manipulation problems common in robotics. Video, additional material and results can be found here: https://sites.google.com/view/stein-prm.

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IROS Conference 2021 Conference Paper

Bayesian Residual Policy Optimization: : Scalable Bayesian Reinforcement Learning with Clairvoyant Experts

Gilwoo Lee
Brian Hou
Sanjiban Choudhury
Siddhartha S. Srinivasa

Informed and robust decision making in the face of uncertainty is critical for robots operating in unstructured environments. We formulate this as Bayesian Reinforcement Learning over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorithms scale poorly to continuous state and action spaces. We build on the following insight: in the absence of uncertainty, each latent MDP is easier to solve. We first obtain an ensemble of experts, one for each latent MDP, and fuse their advice to compute a baseline policy. Next, we train a Bayesian residual policy to improve upon the ensemble’s recommendation and learn to reduce uncertainty. Our algorithm, Bayesian Residual Policy Optimization (BRPO), imports the scalability of policy gradient methods and task-specific expert skills. BRPO significantly improves the ensemble of experts and drastically outperforms existing adaptive RL methods, both in simulated and physical robot experiments.

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ICRA Conference 2020 Conference Paper

Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges

Brian Hou
Sanjiban Choudhury
Gilwoo Lee
Aditya Mandalika
Siddhartha S. Srinivasa

Collision checking is a computational bottleneck in motion planning, requiring lazy algorithms that explicitly reason about when to perform this computation. Optimism in the face of collision uncertainty minimizes the number of checks before finding the shortest path. However, this may take a prohibitively long time to compute, with no other feasible paths discovered during this period. For many real-time applications, we instead demand strong anytime performance, defined as minimizing the cumulative lengths of the feasible paths yielded over time. We introduce Posterior Sampling for Motion Planning (PSMP), an anytime lazy motion planning algorithm that leverages learned posteriors on edge collisions to quickly discover an initial feasible path and progressively yield shorter paths. PSMP obtains an expected regret bound of Õ(√(SAT)) and outperforms comparative baselines on a set of 2D and 7D planning problems.

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ICAPS Conference 2017 Conference Paper

Efficient Motion Planning for Problems Lacking Optimal Substructure

Oren Salzman
Brian Hou
Siddhartha S. Srinivasa

We consider the motion-planning problem of planning a collision-free path of a robot in the presence of risk zones. The robot is allowed to travel in these zones but is penalized in a super-linear fashion for consecutive accumulative time spent there. We suggest a natural cost function that balances path length and risk-exposure time. Specifically, we consider the discrete setting where we are given a graph, or a roadmap, and we wish to compute the minimal-cost path under this cost function. Interestingly, paths defined using our cost function do not have an optimal substructure. Namely, subpaths of an optimal path are not necessarily optimal. Thus, the Bellman condition is not satisfied and standard graph-search algorithms such as Dijkstra cannot be used. We present a path-finding algorithm, which can be seen as a natural generalization of Dijkstra’s algorithm. Our algorithm runs in O ((n B · n) log(n B · n) + n B · m) time, where n and m are the number of vertices and edges of the graph, respectively, and n B is the number of intersections between edges and the boundary of the risk zone. We present simulations on robotic platforms demonstrating both the natural paths produced by our cost function and the computational efficiency of our algorithm.

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ICRA Conference 2016 Conference Paper

Dex-Net 1. 0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards

Jeffrey Mahler
Florian T. Pokorny
Brian Hou
Melrose Roderick
Michael Laskey
Mathieu Aubry
Kai Kohlhoff
Torsten Kröger

This paper presents the Dexterity Network (Dex-Net) 1. 0, a dataset of 3D object models and a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust grasp planning. The algorithm uses a Multi- Armed Bandit model with correlated rewards to leverage prior grasps and 3D object models in a growing dataset that currently includes over 10, 000 unique 3D object models and 2. 5 million parallel-jaw grasps. Each grasp includes an estimate of the probability of force closure under uncertainty in object and gripper pose and friction. Dex-Net 1. 0 uses Multi-View Convolutional Neural Networks (MV-CNNs), a new deep learning method for 3D object classification, to provide a similarity metric between objects, and the Google Cloud Platform to simultaneously run up to 1, 500 virtual cores, reducing experiment runtime by up to three orders of magnitude. Experiments suggest that correlated bandit techniques can use a cloud-based network of object models to significantly reduce the number of samples required for robust grasp planning. We report on system sensitivity to variations in similarity metrics and in uncertainty in pose and friction. Code and updated information is available at http://berkeleyautomation.github.io/dex-net/.

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