8 accepted papers
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, whe…
Sampling-based motion planners rely on incre-mental densification to discover progressively shorter paths. After computing feasible path \xi\xi between start x_{s}x_{s} and goal x_{t}x_{t}, the Informed Set (IS) prunes the configuration space \mathcal{X}\mathcal{X} by conservatively eliminating poin…
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 w…
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…
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 algorit…
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 prohib…
Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribution over latent model parameters given a history…
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 o