Adaptive-model Optimization for Robust and Dynamic Bipedal Jumping

核心概念

A novel adaptive-model optimization framework is proposed to efficiently generate accurate and hardware-realizable bipedal jumping trajectories, leveraging adaptive dynamics models and adaptive sampling frequencies during different phases of the jumping motion.

摘要

The proposed Adaptive-model Optimization framework consists of two key components:

1. Adaptive-model Trajectory Optimization (TO):

   • Utilizes three different dynamics models with varied fidelity to capture the model requirements at different phases of the jumping motion:
     • Takeoff phase: 3-link inverted pendulum dynamics
     • Flight phase: Multi-Rigid-Body Dynamics (MRBD)
     • Landing phase: Single-Rigid-Body Dynamics (SRBD)
   • Adaptively adjusts the sampling frequencies of the TO during each phase to balance trajectory accuracy and computation efficiency.
   • Formulates the TO as a nonlinear programming (NLP) problem and efficiently solves it in a few seconds.

2. Adaptive-frequency Model Predictive Control (MPC):

   • Operates at adaptive sampling frequencies synchronized with the TO trajectories to effectively track the jumping motions.
   • Leverages the SRBD model for real-time control and balancing.
   • Works in conjunction with joint-space PD control for trajectory tracking.

The proposed framework has been validated through extensive hardware experiments on the HECTOR bipedal robot. Key results include:

• Demonstration of robust and dynamic jumps covering a distance of up to 40 cm (57% of robot height).
• Validation of 90% success rate in 53 jumping experiments with varied trajectories.
• Successful demonstration of continuous bipedal jumping over discrete terrain with height perturbations up to 20 cm.
• Effective balancing and recovery after large freefall impacts.

The adaptive-model optimization approach enables the generation of accurate and hardware-realizable bipedal jumping trajectories while maintaining efficient computation, addressing the key challenges in dynamic bipedal jumping control.

統計資料

The robot can jump up to 40 cm in distance, which is 57% of its height.
The robot achieved a 90% success rate in 53 jumping experiments.
The robot can jump continuously over discrete terrain with height perturbations up to 20 cm.

引述

"Dynamic and continuous jumping remains an open yet challenging problem in bipedal robot control."
"Allowing adaptivity of the modeling in optimization problems has become a popular strategy in model-based control."
"Conventional model-based tracking control strategies use fixed-frequency control that is unsynchronized with optimal trajectories, which does not fully leverage the model resolution for effective tracking performance."

從以下內容提煉的關鍵洞見

Continuous Dynamic Bipedal Jumping via Adaptive-model Optimization

by Junheng Li,O... 於 arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11807.pdf

深入探究

How can the proposed adaptive-model optimization framework be extended to handle more complex terrain conditions, such as sloped or uneven surfaces?

The adaptive-model optimization framework can be extended to handle more complex terrain conditions by incorporating additional dynamics models and constraints that account for the specific challenges posed by sloped or uneven surfaces. For sloped surfaces, the framework can include models that consider the effects of gravity acting along the incline, adjusting the trajectory planning and control strategies accordingly. Constraints related to the slope angle and friction coefficients can be integrated into the optimization problem to ensure stability and efficient locomotion on such terrains.
When dealing with uneven surfaces, the framework can incorporate adaptive models that account for variations in terrain height and irregularities. By including models that capture the interaction between the robot's feet and the ground, the optimization process can generate trajectories that adapt to the changing terrain conditions. Constraints related to foot clearance, contact forces, and foot placement can be included to ensure safe and effective locomotion on uneven surfaces.
Furthermore, the adaptive-frequency MPC can be enhanced to dynamically adjust the control inputs based on real-time feedback from sensors that detect terrain variations. By continuously updating the control strategy to respond to changes in terrain conditions, the robot can navigate complex terrains with agility and stability.

How can the proposed adaptive-model optimization framework be extended to handle more complex terrain conditions, such as sloped or uneven surfaces?

The adaptive-model optimization framework can be extended to handle more complex terrain conditions by incorporating additional dynamics models and constraints that account for the specific challenges posed by sloped or uneven surfaces. For sloped surfaces, the framework can include models that consider the effects of gravity acting along the incline, adjusting the trajectory planning and control strategies accordingly. Constraints related to the slope angle and friction coefficients can be integrated into the optimization problem to ensure stability and efficient locomotion on such terrains.
When dealing with uneven surfaces, the framework can incorporate adaptive models that account for variations in terrain height and irregularities. By including models that capture the interaction between the robot's feet and the ground, the optimization process can generate trajectories that adapt to the changing terrain conditions. Constraints related to foot clearance, contact forces, and foot placement can be included to ensure safe and effective locomotion on uneven surfaces.
Furthermore, the adaptive-frequency MPC can be enhanced to dynamically adjust the control inputs based on real-time feedback from sensors that detect terrain variations. By continuously updating the control strategy to respond to changes in terrain conditions, the robot can navigate complex terrains with agility and stability.

How can the proposed adaptive-model optimization framework be extended to handle more complex terrain conditions, such as sloped or uneven surfaces?

The adaptive-model optimization framework can be extended to handle more complex terrain conditions by incorporating additional dynamics models and constraints that account for the specific challenges posed by sloped or uneven surfaces. For sloped surfaces, the framework can include models that consider the effects of gravity acting along the incline, adjusting the trajectory planning and control strategies accordingly. Constraints related to the slope angle and friction coefficients can be integrated into the optimization problem to ensure stability and efficient locomotion on such terrains.
When dealing with uneven surfaces, the framework can incorporate adaptive models that account for variations in terrain height and irregularities. By including models that capture the interaction between the robot's feet and the ground, the optimization process can generate trajectories that adapt to the changing terrain conditions. Constraints related to foot clearance, contact forces, and foot placement can be included to ensure safe and effective locomotion on uneven surfaces.
Furthermore, the adaptive-frequency MPC can be enhanced to dynamically adjust the control inputs based on real-time feedback from sensors that detect terrain variations. By continuously updating the control strategy to respond to changes in terrain conditions, the robot can navigate complex terrains with agility and stability.

What are the potential limitations of the SRBD-based MPC approach in terms of handling more dynamic and agile behaviors beyond jumping?

While the SRBD-based MPC approach is effective for handling jumping behaviors, it may have limitations when dealing with more dynamic and agile behaviors beyond jumping. Some potential limitations include:

Complexity of Dynamics: SRBD models may oversimplify the dynamics of the robot, especially in scenarios where multiple contact points or complex interactions with the environment are involved. This simplification may limit the ability of the MPC to capture the full range of dynamic behaviors required for tasks like running or climbing.

Real-time Adaptability: SRBD models may not be as adaptable in real-time to sudden changes in the environment or unexpected disturbances. Agile behaviors often require rapid adjustments in control inputs, which may be challenging to achieve with a simplified rigid-body model.

Contact Dynamics: SRBD models may struggle to accurately represent the complex contact dynamics that occur during dynamic locomotion tasks. Tasks like running or climbing involve intricate interactions between the robot and the environment, which may not be fully captured by a single rigid-body model.

Optimization Complexity: As the complexity of the behavior increases, the optimization problem associated with the SRBD-based MPC approach may become more computationally intensive. This could lead to longer computation times and potential challenges in real-time implementation for highly dynamic tasks.

To address these limitations, a more sophisticated modeling approach that incorporates multi-body dynamics, compliant contact models, and adaptive control strategies may be necessary to handle the diverse range of dynamic and agile behaviors beyond jumping.

What are the potential limitations of the SRBD-based MPC approach in terms of handling more dynamic and agile behaviors beyond jumping?

While the SRBD-based MPC approach is effective for handling jumping behaviors, it may have limitations when dealing with more dynamic and agile behaviors beyond jumping. Some potential limitations include:

Complexity of Dynamics: SRBD models may oversimplify the dynamics of the robot, especially in scenarios where multiple contact points or complex interactions with the environment are involved. This simplification may limit the ability of the MPC to capture the full range of dynamic behaviors required for tasks like running or climbing.

Real-time Adaptability: SRBD models may not be as adaptable in real-time to sudden changes in the environment or unexpected disturbances. Agile behaviors often require rapid adjustments in control inputs, which may be challenging to achieve with a simplified rigid-body model.

Contact Dynamics: SRBD models may struggle to accurately represent the complex contact dynamics that occur during dynamic locomotion tasks. Tasks like running or climbing involve intricate interactions between the robot and the environment, which may not be fully captured by a single rigid-body model.

Optimization Complexity: As the complexity of the behavior increases, the optimization problem associated with the SRBD-based MPC approach may become more computationally intensive. This could lead to longer computation times and potential challenges in real-time implementation for highly dynamic tasks.

To address these limitations, a more sophisticated modeling approach that incorporates multi-body dynamics, compliant contact models, and adaptive control strategies may be necessary to handle the diverse range of dynamic and agile behaviors beyond jumping.

What are the potential limitations of the SRBD-based MPC approach in terms of handling more dynamic and agile behaviors beyond jumping?

While the SRBD-based MPC approach is effective for handling jumping behaviors, it may have limitations when dealing with more dynamic and agile behaviors beyond jumping. Some potential limitations include:

Complexity of Dynamics: SRBD models may oversimplify the dynamics of the robot, especially in scenarios where multiple contact points or complex interactions with the environment are involved. This simplification may limit the ability of the MPC to capture the full range of dynamic behaviors required for tasks like running or climbing.

Real-time Adaptability: SRBD models may not be as adaptable in real-time to sudden changes in the environment or unexpected disturbances. Agile behaviors often require rapid adjustments in control inputs, which may be challenging to achieve with a simplified rigid-body model.

Contact Dynamics: SRBD models may struggle to accurately represent the complex contact dynamics that occur during dynamic locomotion tasks. Tasks like running or climbing involve intricate interactions between the robot and the environment, which may not be fully captured by a single rigid-body model.

Optimization Complexity: As the complexity of the behavior increases, the optimization problem associated with the SRBD-based MPC approach may become more computationally intensive. This could lead to longer computation times and potential challenges in real-time implementation for highly dynamic tasks.

To address these limitations, a more sophisticated modeling approach that incorporates multi-body dynamics, compliant contact models, and adaptive control strategies may be necessary to handle the diverse range of dynamic and agile behaviors beyond jumping.