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Toward Optimal Active Learning through Sampling Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These…
Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments
This work presents a method of jointly optimizing polynomial path segments in an unconstrained quadratic program that is numerically stable for high-order polynomials and large numbers of segments, and is easily formulated for efficient sparse computation.
Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera
A system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight, which enables 3D flight in cluttered environments using only onboard sensor data.
Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation
This paper describes a new model for understanding natural language commands given to autonomous systems that perform navigation and mobile manipulation in semi-structured environments that dynamically instantiates a probabilistic graphical model for a particular natural language command according to the command's hierarchical and compositional semantic structure.
Rapidly-exploring Random Belief Trees for motion planning under uncertainty
The algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration results in a search tree in belief space that provably converges to the optimal path.
Towards robotic assistants in nursing homes: Challenges and results
The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance
It is shown that planning in belief space can be performed efficiently for linear Gaussian systems by using a factored form of the covariance matrix, allowing several prediction and measurement steps to be combined into a single linear transfer function, leading to very efficient posterior belief prediction during planning.
Perspectives on standardization in mobile robot programming: the Carnegie Mellon Navigation (CARMEN) Toolkit
- Michael Montemerlo, N. Roy, S. Thrun
- Computer ScienceProceedings IEEE/RSJ International Conference on…
- 27 October 2003
The authors' open-source robot control software, the Carnegie Mellon Navigation (CARMEN) Toolkit, is described, which chooses not to adopt strict software standards, but to instead focus on good design practices.
MINERVA: a second-generation museum tour-guide robot
- S. Thrun, Maren Bennewitz, D. Schulz
- Computer ScienceProceedings IEEE International Conference on…
- 10 May 1999
An interactive tour-guide robot is described, which was successfully exhibited in a Smithsonian museum, and uses learning pervasively at all levels of the software architecture to address issues such as safe navigation in unmodified and dynamic environments, and short-term human-robot interaction.
Spoken Dialogue Management Using Probabilistic Reasoning
This work uses a Partially Observable Markov Decision Process (POMDP)-style approach to generate dialogue strategies by inverting the notion of dialogue state; the state represents the user's intentions, rather than the system state.