ContactNet: An Efficient Online Multi-Contact Planner for Acyclic Legged Robot Locomotion

Core Concepts

ContactNet, a fast neural network-based contact planner, can automatically generate acyclic gait sequences and select optimal footholds in complex environments, enabling online multi-contact planning for legged robot locomotion.

Abstract

The paper presents ContactNet, a novel approach for online multi-contact planning in legged robot locomotion. ContactNet is a neural network-based contact planner that can rapidly generate acyclic gait sequences and select optimal footholds, even in the presence of terrain constraints such as stepping stones.
The key highlights are:

ContactNet uses a multi-output regression neural network to rank a discrete set of possible footholds based on a novel cost function that considers trajectory optimization, stability, and robustness. This allows for fast online selection of the best feasible foothold.

The low computational time of ContactNet, around 1 ms, enables its integration into an MPC framework, where it can provide contact plans concurrently with a trajectory optimizer.

Extensive simulation results with the Solo12 quadruped robot demonstrate the effectiveness of ContactNet in navigating complex terrains with holes and stepping stones. The approach can automatically adapt the gait sequence as needed, in contrast to methods that rely on predefined cyclic gaits.

The ContactNet is shown to be robust to measurement uncertainties, suggesting its potential for successful transfer to real-world deployment.

The proposed ContactNet framework addresses the limitations of existing contact planners, which are either too slow for real-time use or restricted to cyclic gaits. By combining fast online contact planning with trajectory optimization, the approach enables legged robots to navigate challenging environments with agility and adaptability.

Stats

The average computation time for a complete iteration of the ContactNet, i.e. computation of 3 subsequent actions, is 1.6 ms.

Quotes

"The low computation time, in the order of 1 ms, makes possible the execution of the contact planner concurrently with a trajectory optimizer in a Model Predictive Control (MPC) fashion."
"ContactNet ranks discretized stepping regions, allowing to quickly choose the best feasible solution, even in complex environments."

Key Insights Distilled From

ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion

by Angelo Bratt... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2209.15566.pdf

Deeper Inquiries

How could the ContactNet framework be extended to handle uneven terrain, where the 3D stepping region needs to be discretized?

To extend the ContactNet framework to handle uneven terrain with a 3D stepping region, the following steps could be taken:

Discretization of 3D Stepping Region: The stepping region in 3D space would need to be discretized into a grid of feasible foothold locations. This grid would encompass not only the X and Y dimensions but also the Z dimension to account for varying terrain heights.

Training Data Generation: Similar to the flat terrain scenario, a dataset would need to be generated for the ContactNet to learn how to rank the foothold locations in the 3D space. This dataset would include various terrain configurations with different heights and obstacles.

Training the ContactNet: The ContactNet would need to be trained on the new dataset that includes 3D terrain information. The neural network architecture may need to be adjusted to accommodate the additional dimensionality of the input data.

Integration with Trajectory Optimization: The ContactNet would still need to work in conjunction with the trajectory optimizer to generate optimal trajectories based on the selected footholds. The trajectory optimization process would need to consider the 3D nature of the terrain.

Real-time Adaptation: The ContactNet should be able to adapt the foothold selection during the swing phase as well, taking into account the changing terrain conditions. This would require continuous prediction and adjustment based on the evolving environment.

How could the ContactNet be further improved to update the foothold during the swing phase, enabling even faster adaptation to changes in the environment?

To enhance the ContactNet for faster adaptation during the swing phase, the following improvements could be implemented:

Real-time Feedback Loop: Implement a real-time feedback loop where the ContactNet continuously evaluates the terrain and updates the foothold selection during the swing phase based on the latest information. This would require rapid decision-making capabilities.

Dynamic Foothold Ranking: Develop a mechanism within the ContactNet that dynamically ranks the footholds based on real-time sensor feedback and environmental changes. This would ensure that the most suitable foothold is selected at each moment.

Predictive Modeling: Integrate predictive modeling techniques into the ContactNet to anticipate potential changes in the terrain and proactively adjust the foothold selection before the swing phase. This predictive capability would enable faster adaptation.

Parallel Processing: Utilize parallel processing capabilities to expedite the computation of optimal footholds during the swing phase. This would involve optimizing the neural network architecture and training process for faster inference.

Adaptive Learning: Implement adaptive learning algorithms that allow the ContactNet to learn and improve its decision-making process in real-time based on the feedback received during locomotion. This would enable continuous refinement of the foothold selection strategy.

What other types of legged robots, beyond the Solo12 quadruped, could benefit from the ContactNet approach for online multi-contact planning?

Several types of legged robots could benefit from the ContactNet approach for online multi-contact planning, including:

Hexapod Robots: Robots with six legs could benefit from ContactNet to optimize multi-contact planning for enhanced stability and maneuverability, especially in complex terrains.

Bipedal Robots: Bipedal robots could utilize ContactNet for dynamic footstep planning and gait optimization, enabling them to navigate challenging environments with improved efficiency and adaptability.

Octopod Robots: Robots with eight legs could leverage ContactNet for coordinating multiple contact points simultaneously, allowing for more versatile locomotion capabilities across various terrains.

Centaur Robots: Hybrid robots combining wheeled and legged locomotion could use ContactNet to plan and execute multi-contact strategies for seamless transitions between different modes of movement.

Snake-like Robots: Even robots with serpentine or snake-like locomotion mechanisms could benefit from ContactNet by optimizing contact points along the body to enhance stability and control during traversal.

By applying the ContactNet framework to these diverse types of legged robots, it can enable them to adapt to changing environments, improve robustness, and achieve more agile and efficient locomotion capabilities.