Quantitative Benchmarking and Reward Design for Robust Humanoid Standing and Walking

Core Concepts

A quantitative benchmarking framework and a minimally-constraining reward function are proposed to systematically evaluate and improve the real-world performance of humanoid standing and walking controllers.

Abstract

The paper introduces a set of quantitative real-world benchmarks to evaluate key aspects of humanoid standing and walking (SaW) controllers, including disturbance rejection, command following accuracy, and energy efficiency. The authors also revisit reward function design for training SaW controllers, proposing a minimally-constraining reward function that avoids unnecessary constraints.
The benchmarking framework is used to evaluate and compare three SaW controllers for the Digit humanoid robot: the manufacturer-provided controller, a state-of-the-art clock-based reinforcement learning (RL) controller, and the authors' newly trained Single Contact RL controller. The results reveal trade-offs between the controllers and guide further improvements, leading to the development of the Single Contact++ RL controller, which outperforms the other approaches on the proposed benchmarks.
The key insights from the paper are:

Systematic real-world benchmarking is crucial for advancing humanoid SaW control, as it can uncover unexpected failure modes and guide targeted improvements.
Minimally-constraining reward functions can lead to more flexible and robust SaW behaviors compared to highly prescriptive reward designs.
The proposed benchmarking framework and reward function serve as a starting point for continuous, measurable progress in real-world humanoid locomotion capabilities.

Stats

The robot was able to withstand lateral pushes of up to 150N for 500ms and sagittal pushes of up to 200N for 500ms without falling.
The Single Contact++ RL controller achieved perfect disturbance rejection in the tested range of impulses.
The RL controllers had significantly lower drift during in-place rotation compared to the manufacturer-provided controller.
The RL controllers had higher energy usage per meter traveled compared to the manufacturer-provided controller.

Quotes

"The failure to establish objective measurements of real-world performance that can be easily implemented by most researchers has made it difficult to judge progress and compare competing methodologies."
"Importantly, we are not claiming that the current SaW benchmarks are complete or that the new reward function is truly minimal and cannot be further improved. Instead, we view the SaW benchmarks and new reward function as starting points on a trajectory of continual, measurable real-world improvements."

Key Insights Distilled From

Revisiting Reward Design and Evaluation for Robust Humanoid Standing and Walking

by Bart van Mar... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19173.pdf

Deeper Inquiries

How can the proposed benchmarking framework be extended to evaluate other aspects of humanoid robot performance, such as manipulation or navigation capabilities

The proposed benchmarking framework for evaluating humanoid robot performance can be extended to assess other aspects such as manipulation or navigation capabilities by adapting the metrics and testing procedures.
For manipulation capabilities, the benchmarking could include tasks like object grasping, lifting, and placing with varying weights and shapes. Metrics could measure accuracy, speed, and efficiency of manipulation tasks. Testing fixtures could be designed to simulate real-world manipulation scenarios, such as picking up objects from different heights or orientations.
In the case of navigation capabilities, benchmarks could involve obstacle avoidance, path planning, and localization accuracy. Metrics might include time to reach a target, deviation from the planned path, and robustness to dynamic environments. Testing setups could incorporate dynamic obstacles, varying terrain types, and localization challenges to evaluate the robot's navigation performance in diverse scenarios.
By expanding the benchmarking framework to cover manipulation and navigation capabilities, researchers can gain a comprehensive understanding of a humanoid robot's overall functionality and performance in real-world applications.

What are the potential limitations or biases introduced by the specific choice of disturbance types and magnitudes used in the benchmarking procedure, and how can these be addressed

The specific choice of disturbance types and magnitudes used in the benchmarking procedure may introduce limitations and biases that could impact the evaluation of humanoid robot performance.
One potential limitation is that the selected disturbances may not fully represent the range of real-world challenges that a humanoid robot could encounter. To address this, researchers could expand the range of disturbance types, such as introducing dynamic disturbances like moving obstacles or varying terrain conditions. By incorporating a wider variety of disturbances, the benchmarking procedure can provide a more comprehensive assessment of the robot's robustness and adaptability.
Another limitation could arise from the fixed magnitudes of the disturbances, which may not scale appropriately to different robot sizes or capabilities. Researchers could address this by scaling the disturbance magnitudes relative to the robot's size and weight, ensuring that the evaluations are fair and consistent across different robot platforms.
Additionally, biases may be introduced if the disturbances are not randomly applied or if the testing environment does not accurately reflect real-world conditions. To mitigate these biases, researchers should randomize the timing and direction of disturbances and ensure that the testing setup closely mimics the challenges that a humanoid robot would face in practical scenarios.
By addressing these limitations and biases, the benchmarking procedure can provide more reliable and informative evaluations of humanoid robot performance under various disturbance conditions.

How can the insights from this work on minimally-constraining reward functions be applied to other areas of robotics and AI, where overly prescriptive objective functions may be hindering progress

The insights from this work on minimally-constraining reward functions can be applied to other areas of robotics and AI where overly prescriptive objective functions may be hindering progress.
In fields like reinforcement learning and autonomous systems, where reward design plays a crucial role in shaping agent behavior, the concept of minimally-constraining rewards can lead to more flexible and adaptive learning algorithms. By focusing on reward functions that guide behavior without imposing unnecessary constraints, researchers can enable agents to explore a wider range of strategies and solutions.
For autonomous vehicles, minimizing constraints in reward functions could enhance decision-making processes and improve adaptability to complex and dynamic environments. By allowing vehicles to learn from experience without rigid constraints, they can better handle unforeseen situations and edge cases on the road.
In industrial automation, applying minimally-constraining rewards can lead to more efficient and robust control policies for robotic systems. By designing reward functions that prioritize task completion while allowing for flexibility in execution, robots can optimize their actions based on real-time feedback and environmental changes.
Overall, the principles of minimally-constraining reward functions can drive innovation and progress in various robotics and AI applications by promoting adaptive learning, robust performance, and enhanced autonomy in intelligent systems.