Dynamics

@inproceedings{Sivaramakrishnan:2024aa,
title = {Roadmaps with Gaps Over Controllers: Achieving Efficiency in Planning under Dynamics},
author = {A Sivaramakrishnan and S Tangirala and E Granados and N Carver and K Bekris},
url = {https://arxiv.org/abs/2310.03239},
year = {2024},
date = {2024-10-01},
booktitle = {IEEE/RSJ Intern. Conference on Intelligent Robots and Systems (IROS)},
address = {Abu Dhabi, United Arab Emirates},
abstract = {This paper aims to improve the computational efficiency of motion planning for mobile robots with non-trivial dynamics through the use of learned controllers. It adopts a decoupled strategy, where a system-specific controller is first trained offline in an empty environment to deal with the robot's dynamics. For a target environment, the proposed approach constructs offline a data structure, a ``Roadmap with Gaps,'' to approximately learn how to solve planning queries in this environment using the learned controller. The nodes of the roadmap correspond to local regions. Edges correspond to applications of the learned control policy that approximately connect these regions. Gaps arise because the controller does not perfectly connect pairs of individual states along edges. Online, given a query, a tree sampling-based motion planner uses the roadmap so that the tree's expansion is informed towards the goal region. The tree expansion selects local subgoals given a wavefront on the roadmap that guides towards the goal. When the controller cannot reach a subgoal region, the planner resorts to random exploration to maintain probabilistic completeness and asymptotic optimality. The accompanying experimental evaluation shows that the approach significantly improves the computational efficiency of motion planning on various benchmarks, including physics-based vehicular models on uneven and varying friction terrains as well as a quadrotor under air pressure effects.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Wang:2023aa,
title = {Real2sim2real Transfer for Control of Cable-Driven Robots Via a Differentiable Physics Engine},
author = {K Wang and W Johnson and S Lu and X Huang and J Booth and R Kramer-Bottiglio and M Aanjaneya and K Bekris},
url = {https://arxiv.org/abs/2209.06261},
year = {2023},
date = {2023-10-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address = {Detroit, MI},
abstract = {Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. Physics-based simulation is a promising avenue for developing locomotion policies that can be transferred to real robots. Nevertheless, modeling tensegrity robots is a complex task due to a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy for tensegrity robots. This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot. These data include offline measurements of physical properties, such as mass and geometry for various robot components, and the observation of a trajectory using a random control policy. With the data from the real robot, the engine can be iteratively refined and used to discover locomotion policies that are directly transferable to the real robot. Beyond the R2S2R pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function for matching tensegrity locomotion gaits, and a trajectory segmentation technique that avoids conflicts in gradient evaluation during training. Multiple iterations of the R2S2R process are demonstrated and evaluated on a real 3-bar tensegrity robot.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}