54 research outputs found
Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization
A significant challenge in manipulation motion planning is to ensure agility
in the face of unpredictable changes during task execution. This requires the
identification and possible modification of suitable joint-space trajectories,
since the joint velocities required to achieve a specific endeffector motion
vary with manipulator configuration. For a given manipulator configuration, the
joint space-to-task space velocity mapping is characterized by a quantity known
as the manipulability index. In contrast to previous control-based approaches,
we examine the maximization of manipulability during planning as a way of
achieving adaptable and safe joint space-to-task space motion mappings in
various scenarios. By representing the manipulator trajectory as a
continuous-time Gaussian process (GP), we are able to leverage recent advances
in trajectory optimization to maximize the manipulability index during
trajectory generation. Moreover, the sparsity of our chosen representation
reduces the typically large computational cost associated with maximizing
manipulability when additional constraints exist. Results from simulation
studies and experiments with a real manipulator demonstrate increases in
manipulability, while maintaining smooth trajectories with more dexterous (and
therefore more agile) arm configurations.Comment: In Proceedings of the IEEE International Conference on Intelligent
Robots and Systems (IROS'19), Macau, China, Nov. 4-8, 201
Computer-Assisted Proving of Combinatorial Conjectures Over Finite Domains: A Case Study of a Chess Conjecture
There are several approaches for using computers in deriving mathematical
proofs. For their illustration, we provide an in-depth study of using computer
support for proving one complex combinatorial conjecture -- correctness of a
strategy for the chess KRK endgame. The final, machine verifiable, result
presented in this paper is that there is a winning strategy for white in the
KRK endgame generalized to board (for natural greater than
). We demonstrate that different approaches for computer-based theorem
proving work best together and in synergy and that the technology currently
available is powerful enough for providing significant help to humans deriving
complex proofs
A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image
Autonomous manipulation systems operating in domains where human intervention
is difficult or impossible (e.g., underwater, extraterrestrial or hazardous
environments) require a high degree of robustness to sensing and communication
failures. Crucially, motion planning and control algorithms require a stream of
accurate joint angle data provided by joint encoders, the failure of which may
result in an unrecoverable loss of functionality. In this paper, we present a
novel method for retrieving the joint angles of a robot manipulator using only
a single RGB image of its current configuration, opening up an avenue for
recovering system functionality when conventional proprioceptive sensing is
unavailable. Our approach, based on a distance-geometric representation of the
configuration space, exploits the knowledge of a robot's kinematic model with
the goal of training a shallow neural network that performs a 2D-to-3D
regression of distances associated with detected structural keypoints. It is
shown that the resulting Euclidean distance matrix uniquely corresponds to the
observed configuration, where joint angles can be recovered via
multidimensional scaling and a simple inverse kinematics procedure. We evaluate
the performance of our approach on real RGB images of a Franka Emika Panda
manipulator, showing that the proposed method is efficient and exhibits solid
generalization ability. Furthermore, we show that our method can be easily
combined with a dense refinement technique to obtain superior results.Comment: IFAC 202
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