Learning Skill-based Industrial Robot Tasks with User Priors (2208.01605v1)

Abstract: Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutions could be found. Interestingly, such prior knowledge is not exploited in current robot learning frameworks. We introduce an approach that combines user priors and Bayesian optimization to allow fast optimization of robot industrial tasks at robot deployment time. We evaluate our method on three tasks that are learned in simulation as well as on two tasks that are learned directly on a real robot system. Additionally, we transfer knowledge from the corresponding simulation tasks by automatically constructing priors from well-performing configurations for learning on the real system. To handle potentially contradicting task objectives, the tasks are modeled as multi-objective problems. Our results show that operator priors, both user-specified and transferred, vastly accelerate the discovery of rich Pareto fronts, and typically produce final performance far superior to proposed baselines.

Key Insights into Learning Skill-Based Industrial Robot Tasks with User Priors

The paper "Learning Skill-based Industrial Robot Tasks with User Priors," authored by Matthias Mayr et al., presents an innovative approach to enhancing the autonomous learning capabilities of industrial robots. The work integrates user-defined priors with Bayesian optimization to achieve swift and efficient learning of robot tasks. This research specifically addresses challenges associated with parameter selection for complex, contact-rich tasks in manufacturing environments.

Core Contributions

The authors put forth several noteworthy contributions:

1. Integration of User Priors and Bayesian Optimization: The methodology introduced allows for the incorporation of human operator insights into the learning process. Operators can provide educated guesses on where optimal solutions might lie in the parameter space using probability densities as priors.
2. Implementation of Multi-Objective Optimization: The tasks are modeled considering multiple objectives, such as speed and interaction forces, thereby ensuring a balanced approach to optimization. This multi-faceted modeling allows robust solutions across various performance indicators.
3. Knowledge Transfer from Simulations: Significant emphasis is placed on the transfer of successful configurations from simulation environments to real-world tasks. This is facilitated through a probability density function that encapsulates high-performing simulation outcomes which guide real robot learning.
4. Robust Empirical Evaluation: The proposed approach was thoroughly tested on three distinct tasks, both in simulation and real-world settings. The results consistently demonstrated accelerated learning and superior performance when leveraging user and simulation priors.

Numerical Results and Robustness

Numerically, the inclusion of operator priors resulted in an accelerated learning process, benefiting from fewer iterations with enhanced final performance when compared to baseline methods. This was most evident in tasks like object pushing and obstacle avoidance. Furthermore, when learning was transferred from simulated environments to real-world scenarios using learned priors, notable gains were recorded, reducing the typical requirement for manual tuning.

The methodology also exhibited resilience to potentially misleading priors, highlighting its robustness. This aspect points to a diminishing risk associated with inaccurate human input, making the system both adaptive and resilient in diverse settings.

Implications and Future Directions

The development of adaptable industrial robots is crucial for maximizing productivity and flexibility within manufacturing settings. This research contributes a crucial mechanism that combines human intuition with machine learning techniques, paving the way for more autonomous systems capable of rapid adaptation to new tasks with minimal intervention.

From a theoretical perspective, this paper opens avenues for further exploration into multi-objective policy optimization using Bayesian methods. On a practical note, it suggests a potential shift in how robot skills are conceived, leveraging existing knowledge more effectively.

Future work could explore the scalability of this approach across varying robot platforms and task complexities. Additionally, exploring multi-fidelity learning systems could significantly enhance the efficiency and safety of robot setup processes.

Overall, Mayr et al.'s research presents a notable advancement in the field of robotic learning systems, melding industrial needs with state-of-the-art machine learning methods. Through thoughtful integration of operator experience and computational intelligence, this work exemplifies the potential for robotics to evolve into more efficient, adaptable, and autonomous entities within industrial domains.