Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

RoLoMa: Robust Loco-Manipulation for Quadruped Robots with Arms (2203.01446v2)

Published 2 Mar 2022 in cs.RO

Abstract: Deployment of robotic systems in the real world requires a certain level of robustness in order to deal with uncertainty factors, such as mismatches in the dynamics model, noise in sensor readings, and communication delays. Some approaches tackle these issues reactively at the control stage. However, regardless of the controller, online motion execution can only be as robust as the system capabilities allow at any given state. This is why it is important to have good motion plans to begin with, where robustness is considered proactively. To this end, we propose a metric (derived from first principles) for representing robustness against external disturbances. We then use this metric within our trajectory optimization framework for solving complex loco-manipulation tasks. Through our experiments, we show that trajectories generated using our approach can resist a greater range of forces originating from any possible direction. By using our method, we can compute trajectories that solve tasks as effectively as before, with the added benefit of being able to counteract stronger disturbances in worst-case scenarios.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (25)
  1. M. P. Murphy, B. Stephens, Y. Abe, and A. A. Rizzi, “High degree-of-freedom dynamic manipulation,” in Unmanned Systems Technology XIV, R. E. Karlsen, D. W. Gage, C. M. Shoemaker, and G. R. Gerhart, Eds., vol. 8387, International Society for Optics and Photonics.   SPIE, 2012, p. 83870V.
  2. S. Zimmermann, R. Poranne, and S. Coros, “Go Fetch! - Dynamic Grasps using Boston Dynamics Spot with External Robotic Arm,” in IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 4488–4494.
  3. Y. Ma, F. Farshidian, T. Miki, J. Lee, and M. Hutter, “Combining Learning-Based Locomotion Policy With Model-Based Manipulation for Legged Mobile Manipulators,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 2, pp. 2377–2384, 2022.
  4. A. D. Prete and N. Mansard, “Robustness to joint-torque-tracking errors in task-space inverse dynamics,” IEEE Transactions on Robotics (T-RO), vol. 32, no. 5, pp. 1091–1105, 2016.
  5. G. Xin, H.-C. Lin, J. Smith, O. Cebe, and M. Mistry, “A Model-Based Hierarchical Controller for Legged Systems Subject to External Disturbances,” in IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 4375–4382.
  6. J.-P. Sleiman, F. Farshidian, M. V. Minniti, and M. Hutter, “A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation,” IEEE Robotics and Automation Letters (RA-L), vol. 6, no. 3, pp. 4688–4695, 2021.
  7. H. Ferrolho, W. Merkt, V. Ivan, W. Wolfslag, and S. Vijayakumar, “Optimizing Dynamic Trajectories for Robustness to Disturbances Using Polytopic Projections,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 7477–7484.
  8. H. Ferrolho, V. Ivan, W. Merkt, I. Havoutis, and S. Vijayakumar, “Inverse Dynamics vs. Forward Dynamics in Direct Transcription Formulations for Trajectory Optimization,” in IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 12 752–12 758.
  9. C. D. Bellicoso, K. Krämer, M. Stäuble, D. Sako, F. Jenelten, M. Bjelonic, and M. Hutter, “ALMA — Articulated Locomotion and Manipulation for a Torque-Controllable Robot,” in IEEE International Conference on Robotics and Automation (ICRA), 2019, pp. 8477–8483.
  10. S. Caron, Q. Pham, and Y. Nakamura, “Leveraging Cone Double Description for Multi-contact Stability of Humanoids with Applications to Statics and Dynamics,” in Robotics: Science and System, vol. 11, 2015, pp. 1–9.
  11. R. Orsolino, M. Focchi, C. Mastalli, H. Dai, D. G. Caldwell, and C. Semini, “Application of Wrench-Based Feasibility Analysis to the Online Trajectory Optimization of Legged Robots,” IEEE Robotics and Automation Letters (RA-L), vol. 3, no. 4, pp. 3363–3370, 2018.
  12. H. Ferrolho, W. Merkt, C. Tiseo, and S. Vijayakumar, “Residual force polytope: Admissible task-space forces of dynamic trajectories,” Robotics and Autonomous Systems, vol. 142, p. 103814, 2021.
  13. H. R. Tiwary, “On the hardness of computing intersection, union and minkowski sum of polytopes,” Discrete & Computational Geometry, vol. 40, no. 3, pp. 469–479, 2008.
  14. J. Zhen and D. den Hertog, “Computing the Maximum Volume Inscribed Ellipsoid of a Polytopic Projection,” INFORMS Journal on Computing, vol. 30, no. 1, pp. 31–42, 2018.
  15. W. J. Wolfslag, C. McGreavy, G. Xin, C. Tiseo, S. Vijayakumar, and Z. Li, “Optimisation of Body-ground Contact for Augmenting Whole-Body Loco-manipulation of Quadruped Robots,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 3694–3701.
  16. P. G. Gormley, “Stereographic Projection and the Linear Fractional Group of Transformations of Quaternions,” Proceedings of the Royal Irish Academy. Mathematical and Physical Sciences, vol. 51, pp. 67–85, 1945.
  17. G. Terzakis, M. Lourakis, and D. Ait-Boudaoud, “Modified Rodrigues Parameters: An Efficient Representation of Orientation in 3D Vision and Graphics,” Journal of Mathematical Imaging and Vision, vol. 60, pp. 422–442, 2018.
  18. J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim, “Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 1–9.
  19. A. W. Winkler, D. C. Bellicoso, M. Hutter, and J. Buchli, “Gait and Trajectory Optimization for Legged Systems through Phase-based End-Effector Parameterization,” IEEE Robotics and Automation Letters (RA-L), vol. 3, no. 3, pp. 1560–1567, 2018.
  20. S. Tonneau, D. Song, P. Fernbach, N. Mansard, M. Taïx, and A. Del Prete, “SL1M: Sparse L1-norm Minimization for contact planning on uneven terrain,” in IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 6604–6610.
  21. M. Posa, C. Cantu, and R. Tedrake, “A direct method for trajectory optimization of rigid bodies through contact,” The International Journal of Robotics Research (IJRR), vol. 33, no. 1, pp. 69–81, 2014.
  22. J. Bezanson, A. Edelman, S. Karpinski, and V. B. Shah, “Julia: A Fresh Approach to Numerical Computing,” SIAM Review, vol. 59, no. 1, pp. 65–98, 2017.
  23. T. Koolen and contributors, “RigidBodyDynamics.jl,” 2016. [Online]. Available: https://github.com/JuliaRobotics/RigidBodyDynamics.jl
  24. R. A. Waltz, J. L. Morales, J. Nocedal, and D. Orban, “An interior algorithm for nonlinear optimization that combines line search and trust region steps,” Mathematical Programming, vol. 107, no. 3, pp. 391–408, 2006.
  25. C. Mastalli, R. Budhiraja, W. Merkt, G. Saurel, B. Hammoud, M. Naveau, J. Carpentier, L. Righetti, S. Vijayakumar, and N. Mansard, “Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control,” in IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 2536–2542.
Citations (41)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now