A Unified Method for Solving Inverse, Forward, and Hybrid Manipulator Dynamics using Factor Graphs

This paper describes a unified method solving for inverse, forward, and hybrid dynamics problems for robotic manipulators with either open kinematic chains or closed kinematic loops based on factor graphs. Manipulator dynamics is considered to be a well studied problem, and various different algorithms have been developed to solve each type of dynamics problem. However, they are not easily explained in a unified and intuitive way. In this paper, we introduce factor graphs as a unifying graphical language in which not only to solve all types of dynamics problems, but also explain the classical dynamics algorithms in a unified framework

Batch and Incremental Kinodynamic Motion Planning using Dynamic Factor Graphs

This paper presents a kinodynamic motion planner that is able to produce energy efficient motions by taking the full robot dynamics into account, and making use of gravity, inertia, and momentum to reduce the effort. Given a specific goal state for the robot, we use factor graphs and numerical optimization to solve for an optimal trajectory, which meets not only the requirements of collision avoidance, but also all kinematic and dynamic constraints, such as velocity, acceleration and torque limits. By exploiting the sparsity in factor graphs, we can solve a kinodynamic motion planning problem efficiently, on par with existing optimal control methods, and use incremental elimination techniques to achieve an order of magnitude faster replanning

A general framework for modeling and dynamic simulation of multibody systems using factor graphs

In this paper, we present a novel general framework grounded in the factor graph theory to solve kinematic and dynamic problems for multi-body systems. Although the motion of multi-body systems is considered to be a well-studied problem and various methods have been proposed for its solution, a unified approach providing an intuitive interpretation is still pursued. We describe how to build factor graphs to model and simulate multibody systems using both, independent and dependent coordinates. Then, batch optimization or a fixed-lag-smoother can be applied to solve the underlying optimization problem that results in a highly-sparse nonlinear minimization problem. The proposed framework has been tested in extensive simulations and validated against a commercial multibody software. We release a reference implementation as an open-source C++ library, based on the GTSAM framework, a well-known estimation library. Simulations of forward and inverse dynamics are presented, showing comparable accuracy with classical approaches. The proposed factor graph-based framework has the potential to be integrated into applications related with motion estimation and parameter identification of complex mechanical systems, ranging from mechanisms to vehicles, or robot manipulators.Comment: 23 page

In-Hand Object-Dynamics Inference using Tactile Fingertips

Having the ability to estimate an object's properties through interaction will enable robots to manipulate novel objects. Object's dynamics, specifically the friction and inertial parameters have only been estimated in a lab environment with precise and often external sensing. Could we infer an object's dynamics in the wild with only the robot's sensors? In this paper, we explore the estimation of dynamics of a grasped object in motion, with tactile force sensing at multiple fingertips. Our estimation approach does not rely on torque sensing to estimate the dynamics. To estimate friction, we develop a control scheme to actively interact with the object until slip is detected. To robustly perform the inertial estimation, we setup a factor graph that fuses all our sensor measurements on physically consistent manifolds and perform inference. We show that tactile fingertips enable in-hand dynamics estimation of low mass objects.Comment: Accepted at IEEE Transactions on Robotics (T-RO). Website: https://sites.google.com/view/tactile-obj-dynamic