Learned Whole-Body Force Control for Versatile Legged Manipulation

מושגי ליבה

A learned whole-body policy enables direct force control at the end effector of a legged manipulator, enabling compliant interaction, gravity compensation, and forceful whole-body manipulation.

תקציר

The authors propose a method for training a reinforcement learning policy to directly control the force applied at the end effector of a legged manipulator, without requiring access to force sensing. This enables the robot to perform a variety of compliant and forceful manipulation tasks by coordinating its whole body.
The key highlights are:

The policy is trained to track a commanded end effector force while also controlling the end effector position and the robot's base velocity. This allows the robot to switch between position and force control modes as needed for the task.

The force control is achieved without any dedicated force sensors, by leveraging the robot's proprioceptive sensing and a simulated external force field during training. This allows the policy to estimate the applied forces and modulate them as necessary.

The learned whole-body controller enables intuitive teleoperation, where the human operator only commands the manipulator, and the robot's body automatically adjusts to achieve the desired position and force.

Experiments show the policy can track forces up to 90N across a large workspace, perform impedance control, and enable compliant behaviors like kinesthetic teaching and gravity compensation for heavy object manipulation.

The policy also demonstrates effective end effector position tracking, expanding the robot's workspace by 59% compared to the arm alone through whole-body coordination.

Overall, this work presents a novel approach to enable versatile legged manipulation capabilities by learning direct force control, which can unlock a wide range of forceful interaction tasks for legged robots.

סטטיסטיקה

The mean absolute force tracking error is around 5 N for low force targets and remains below 10 N across the entire training range of 0-70 N.
The mean position error is 4.6 cm in x-axis, 4.8 cm in y-axis, and 5.5 cm in z-axis when tracking end effector position commands.

ציטוטים

"To the best of our knowledge, we provide the first deployment of learned whole-body force control in legged manipulators, paving the way for more versatile and adaptable legged robots."
"Our learned force control is particularly useful in various scenarios, such as enabling compliance for kinesthetic demonstration, safer human-robot interaction, and optimizing the whole-body posture for applying large forces across a larger workspace."

תובנות מפתח מזוקקות מ:

Learning Force Control for Legged Manipulation

by Tifanny Port... ב- arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01402.pdf

שאלות מעמיקות

How could the force control policy be extended to handle dynamic, unpredictable contacts and disturbances in real-world environments?

To enhance the force control policy's capability to handle dynamic and unpredictable contacts in real-world environments, several strategies can be implemented. Firstly, incorporating adaptive control techniques can help the policy adjust its parameters in real-time based on the changing environment. This adaptive control mechanism can enable the robot to respond effectively to unexpected disturbances and varying contact conditions. Additionally, integrating robust control methods can improve the policy's resilience to uncertainties and disturbances, ensuring stable performance even in dynamic environments. Furthermore, implementing sensor fusion techniques by combining data from multiple sensors can provide the policy with a more comprehensive understanding of the environment, enabling it to react promptly to sudden changes or disturbances. By combining these approaches, the force control policy can be extended to handle a wide range of dynamic and unpredictable scenarios in real-world settings.

What are the potential limitations of the current approach in terms of precision, stability, and robustness, and how could they be addressed through further research?

While the current approach demonstrates proficiency in force control and compliance, there are potential limitations that need to be addressed for further improvement. In terms of precision, the system may exhibit errors in force tracking and position control, leading to inaccuracies in task execution. To enhance precision, future research could focus on refining the policy architecture, optimizing training algorithms, and incorporating advanced control strategies such as model predictive control. Stability issues may arise when the system encounters complex tasks or disturbances, affecting overall performance. Addressing stability concerns could involve implementing feedback control mechanisms, enhancing disturbance rejection capabilities, and conducting thorough system identification to improve stability margins. Robustness, particularly in handling uncertainties and variations in the environment, is crucial for real-world deployment. Future research efforts could concentrate on robust control design, adaptive learning algorithms, and simulation-to-reality transfer techniques to enhance the system's robustness and resilience to diverse operating conditions.

What other high-level manipulation capabilities could be enabled by combining the learned force control with other skills like dexterous in-hand manipulation, object grasping, and task planning?

By integrating the learned force control with skills in dexterous in-hand manipulation, object grasping, and task planning, the robotic system can achieve advanced high-level manipulation capabilities. The combination of force control with dexterous manipulation skills enables the robot to delicately interact with objects, adjust grip strength based on force feedback, and perform intricate tasks requiring fine motor skills. Object grasping capabilities can be enhanced by integrating force control to apply the optimal amount of force for secure grasping and manipulation of objects with varying shapes and sizes. Task planning, when combined with force control, allows the robot to autonomously plan and execute complex manipulation tasks, optimizing force application based on task requirements and environmental constraints. Overall, the synergy between learned force control and other manipulation skills opens up possibilities for the robot to perform sophisticated tasks with precision, adaptability, and efficiency in diverse real-world scenarios.