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CHOMP: Gradient optimization techniques for efficient motion planning
TLDR
This paper presents CHOMP, a novel method for continuous path refinement that uses covariant gradient techniques to improve the quality of sampled trajectories and relax the collision-free feasibility prerequisite on input paths required by those strategies.
CHOMP: Covariant Hamiltonian optimization for motion planning
In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient
Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic
TLDR
Optimal RRTs (RRT*s) extend R RTs to the problem of finding the optimal solution, but in doing so asymptotically find the optimal path from the initial state to every state in the planning domain.
The YCB object and Model set: Towards common benchmarks for manipulation research
TLDR
The Yale-CMU-Berkeley (YCB) Object and Model set is intended to be used for benchmarking in robotic grasping and manipulation research, and provides high-resolution RGBD scans, physical properties and geometric models of the objects for easy incorporation into manipulation and planning software platforms.
Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs
TLDR
Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques, is presented, which can be viewed as an extension of incremental graph-search techniques to continuous problem domains as well as a generalization of existing sampling- based optimal planners.
A policy-blending formalism for shared control
TLDR
This work proposes an intuitive formalism that captures assistance as policy blending, illustrates how some of the existing techniques for shared control instantiate it, and provides a principled analysis of its main components: prediction of user intent and its arbitration with the user input.
Legibility and predictability of robot motion
TLDR
The findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion, and a formalism to mathematically define and distinguish predictability and legibility of motion is developed.
Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set
TLDR
The Yale-Carnegie Mellon University-Berkeley object and model set is presented, intended to be used to facilitate benchmarking in robotic manipulation research and to enable the community of manipulation researchers to more easily compare approaches and continually evolve standardized benchmarking tests and metrics as the field matures.
Planning-based prediction for pedestrians
TLDR
A novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment is presented and improvement in hindrance-sensitive robot trajectory planning is quantitatively shown.
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