Learning User-Preferred Mappings for Intuitive Robot Control (2007.11627v1)

Abstract: When humans control drones, cars, and robots, we often have some preconceived notion of how our inputs should make the system behave. Existing approaches to teleoperation typically assume a one-size-fits-all approach, where the designers pre-define a mapping between human inputs and robot actions, and every user must adapt to this mapping over repeated interactions. Instead, we propose a personalized method for learning the human's preferred or preconceived mapping from a few robot queries. Given a robot controller, we identify an alignment model that transforms the human's inputs so that the controller's output matches their expectations. We make this approach data-efficient by recognizing that human mappings have strong priors: we expect the input space to be proportional, reversable, and consistent. Incorporating these priors ensures that the robot learns an intuitive mapping from few examples. We test our learning approach in robot manipulation tasks inspired by assistive settings, where each user has different personal preferences and physical capabilities for teleoperating the robot arm. Our simulated and experimental results suggest that learning the mapping between inputs and robot actions improves objective and subjective performance when compared to manually defined alignments or learned alignments without intuitive priors. The supplementary video showing these user studies can be found at: https://youtu.be/rKHka0_48-Q.

Essay on "Learning User-Preferred Mappings for Intuitive Robot Control"

The paper "Learning User-Preferred Mappings for Intuitive Robot Control," authored by Mengxi Li, Dylan P. Losey, Jeannette Bohg, and Dorsa Sadigh, presents an innovative approach to enhancing teleoperation systems by learning user-specific control mappings. The central premise is that human users have inherent preferences and expectations about how their inputs should be translated into robotic actions. This differs from traditional methods where a fixed mapping is predefined by designers, compelling users to adapt their inputs to control robots, which can be inefficient and not user-friendly.

Key Contributions

The paper introduces a data-efficient method for learning the alignment model between human inputs and robotic actions by utilizing intuitive priors expected by humans, such as proportionality, reversibility, and consistency. This approach involves a few robot queries, allowing for personalized and intuitive user-robot interactions without extensive data collection.

1. Formalization of Human Control Priors: The authors formalize intuitive control properties—proportionality, reversibility, and consistency. These properties are critical in capturing the human expectations of control and are used as loss terms in a semi-supervised learning framework to generalize user preferences.
2. Data-Efficient Learning Framework: The framework employs feedforward neural networks to learn the alignment model required to adaptively transform human inputs into suitable robot actions without extensive data requirements. This method is tested on various robotic manipulation tasks, proving effective in assistive contexts where user preferences can vary significantly.
3. User Studies and Evaluation: Through a series of simulation and real-world experiments, the authors evaluate the algorithm, demonstrating improvements in both objective measures (such as task completion time and trajectory accuracy) and subjective user satisfaction. The approach outperformed traditional techniques, illustrating the benefits of personalization in complex robotic tasks.

Numerical Results and Claims

The simulations conducted on a Franka Emika Panda robot arm revealed significant improvements when utilizing the proposed system with intuitive priors over conventional one-size-fits-all approaches and ablations without priors. Users completed tasks like 'Reach & Pour' more accurately and efficiently with an alignment learned from intuitive priors. The results indicate a notable reduction in task completion time and increased trajectory smoothness across various manipulation tasks, highlighting the practicality of employing intuitive priors to expedite learning processes in robotic systems.

Implications and Future Directions

The research has practical implications for designing human-robot interaction systems, particularly in fields requiring high adaptability and user-specific customization, such as assistive robotics and teleoperation in non-industrial settings. The integration of human-intuitive control mappings could facilitate broader adoption and enhance usability across diverse applications, from healthcare to autonomous vehicles.

Theoretically, the paper advances the understanding of intuitive control priors in robotics, laying groundwork for further exploration into personalized learning algorithms that balance the need for specificity with the cost of training data acquisition. Future avenues might explore more sophisticated active learning techniques to further minimize human queries and refine the adaptability of the alignment models across various dynamic environments and tasks.

In summary, the paper contributes a significant methodology for personalized robotic control, demonstrating both empirical and theoretical advancements. By aligning robotic control more closely with user expectations, the proposed approach offers a promising avenue toward more intuitive and user-friendly robotic systems.