Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics

@article{Li2022NaturalMW,
  title={Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics},
  author={Kejun Li and Maegan Tucker and Rachel Gehlhar and Yisong Yue and A. Ames},
  journal={IEEE Robotics and Automation Letters},
  year={2022},
  volume={7},
  pages={4283-4290}
}
Generating stable walking gaits that yield natural locomotion when executed on robotic-assistive devices is a challenging task that often requires hand-tuning by domain experts. This letter presents an alternative methodology, where we propose the addition of musculoskeletal models directly into the gait generation process to intuitively shape the resulting behavior. In particular, we construct a multi-domain hybrid system model that combines the system dynamics with muscle models to represent… 
1 Citations

Figures and Tables from this paper

Overleaf Example

  • 2022

References

SHOWING 1-10 OF 49 REFERENCES

Multi-contact bipedal robotic locomotion

SUMMARY This paper presents a formal framework for achieving multi-contact bipedal robotic walking, and realizes this methodology experimentally on two robotic platforms: AMBER2 and Assume The Robot

Selection of Muscle-Activity-Based Cost Function in Human-in-the-Loop Optimization of Multi-Gait Ankle Exoskeleton Assistance

A muscle-activity-based cost function is constructed based on surface electromyography signals of four lower leg muscles and selected the muscle weight combination by using particle swarm optimization algorithm to compose the cost function with maximum differences between different assistance patterns.

Learning Task Space Actions for Bipedal Locomotion

This work integrates learning a task space policy with a model-based inverse dynamics controller, which translates task space actions into joint-level controls and demonstrates the approach in simulation and shows that the learned policies are able to transfer to the real bipedal robot Cassie.

Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition

A reward-specification framework based on composing simple probabilistic periodic costs on basic forces and velocities is proposed and instantiate this framework to define a parametric reward function with intuitive settings for all common bipedal gaits - standing, walking, hopping, running, and skipping.

Hybrid Machine Learning-Neuromusculoskeletal Modeling for Control of Lower Limb Prosthetics

A novel Machine Learning (ML)driven NMS model able to predict lower limb joint torque only from wearable sensors than can be embedded in a prosthetic device is developed, the first concept of completely wearable and subject-specific EMG-driven N MS model control for lower limb prostheses.

Dynamic Walking: Toward Agile and Efficient Bipedal Robots

The end-to-end process of methods which have proven effective in the literature for achieving dynamic walking on bipedal robots are outlined, from reduced order models that capture essential walking behaviors to hybrid dynamical systems that encode the full order continuous dynamics along with discrete footstrike dynamics.

Musculoskeletal Model for Path Generation and Modification of an Ankle Rehabilitation Robot

Simulation results and experimental findings with healthy subjects using an ankle rehabilitation robot prototype and subsequent statistical analysis validated that path modification based on ankle joint biomechanics results in a reduction in undesired forces experienced by human users during treatment.

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

This work empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization and analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users’ gait preferences.

First Steps Towards Full Model Based Motion Planning and Control of Quadrupeds: A Hybrid Zero Dynamics Approach

  • Wen-Loong MaK. HamedA. Ames
  • Engineering, Mathematics
    2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2019
This paper aims to extend the HZD methods to address walking, ambling and trotting behaviors on a quadrupedal robot by presenting a framework that systematically generates a wide range of optimal trajectories and then provably stabilizes them for the full-order, nonlinear and hybrid dynamical models of quadrupedAL locomotion.