New research from the University of Massachusetts Amherst shows that programming robots to create their own teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture and warehouse automation.

This research was recognized as a finalist for Best Paper Award on Multi-Robot Systems at the IEEE International Conference on Robotics and Automation 2024.

“There’s a long history of debate on whether we want to build a single, powerful humanoid robot that can do all the jobs, or we have a team of robots that can collaborate,” says one of the study authors, Hao Zhang, associate professor in the UMass Amherst Manning College of Information and Computer Sciences and director of the Human-Centered Robotics Lab.

In a manufacturing setting, a robot team can be less expensive because it maximizes the capability of each robot. The challenge then becomes: how do you coordinate a diverse set of robots? Some may be fixed in place, others mobile; some can lift heavy materials, while others are suited to smaller tasks.

As a solution, Zhang and his team created a learning-based approach for scheduling robots called learning for voluntary waiting and subteaming (LVWS).

“Robots have big tasks, just like humans,” says Zhang. “For example, they have a large box that cannot be carried by a single robot. The scenario will need multiple robots to collaboratively work on that.”

The other behavior is voluntary waiting. “We want the robot to be able to actively wait because, if they just choose a greedy solution to always perform smaller tasks that are immediately available, sometimes the bigger task will never be executed,” Zhang explains.

To test their LVWS approach, they gave six robots 18 tasks in a computer simulation and compared their LVWS approach to four other methods. In this computer model, there is a known, perfect solution for completing the scenario in the fastest amount of time.

The researchers ran the different models through the simulation and calculated how much worse each method was compared to this perfect solution, a measure known as suboptimality.

The comparison methods ranged from 11.8% to 23% suboptimal. The new LVWS method was 0.8% suboptimal. “So the solution is close to the best possible or theoretical solution,” says Williard Jose, an author on the paper and a doctoral student in computer science at the Human-Centered Robotics Lab.

How does making a robot wait make the whole team faster? Consider this scenario: You have three robots—two that can lift four pounds each and one that can lift 10 pounds. One of the small robots is busy with a different task and there is a seven-pound box that needs to be moved.

“Instead of that big robot performing that task, it would be more beneficial for the small robot to wait for the other small robot and then they do that big task together because that bigger robot’s resource is better suited to do a different large task,” says Jose.

If it’s possible to determine an optimal answer in the first place, why do robots even need a scheduler? “The issue with using that exact solution is to compute that it takes a really long time,” explains Jose. “With larger numbers of robots and tasks, it’s exponential. You can’t get the optimal solution in a reasonable amount of time.”

When looking at models using 100 tasks, where it is intractable to calculate an exact solution, they found that their method completed the tasks in 22 timesteps compared to 23.05 to 25.85 timesteps for the comparison models.

Zhang hopes this work will help further the progress of these teams of automated robots, particularly when the question of scale comes into play. For instance, he says that a single, humanoid robot may be a better fit in the small footprint of a single-family home, while multi-robot systems are better options for a large industry environment that requires specialized tasks.

Platycodon grandiflorus, commonly known as balloon flower, is renowned for its medicinal properties, primarily due to its rich saponin content. Saponins are known for their anti-inflammatory, anti-cancer, and immune-boosting properties, making P. grandiflorus a valuable plant in traditional medicine.

However, challenges such as low saponin yield and inefficient cultivation practices hinder its widespread use. Addressing these issues is crucial for enhancing the medicinal value and cultivation efficiency of P. grandiflorus.

Due to these challenges, it is imperative to delve into the genetic and molecular mechanisms governing saponin biosynthesis to develop high-yielding cultivars.

Researchers at Northeast Agricultural University, in collaboration with the Key Laboratory of Cold Region Landscape Plants and Applications, published a study in Horticulture Research on 28 February 2024, detailing how the application of methyl jasmonate (MeJA) induces the expression of the PgbHLH28 gene, which is a crucial regulator in saponin accumulation.

The study focused on understanding how MeJA influences saponin biosynthesis in P. grandiflorus. Researchers applied various concentrations of MeJA to P. grandiflorus roots and found that a concentration of 100 μmol/l was optimal for promoting saponin accumulation.

RNA sequencing analysis revealed that the PgbHLH28 gene plays a pivotal role in this process. Overexpression of PgbHLH28 in P. grandiflorus resulted in a significant increase in saponin content, while silencing the gene inhibited saponin synthesis.

Further investigations using yeast one-hybrid and dual luciferase assays demonstrated that PgbHLH28 directly binds to the promoters of the PgHMGR2 and PgDXS2 genes, activating their expression and thereby enhancing saponin biosynthesis.

These findings establish a complex regulatory network involving MeJA and PgbHLH28, which governs the production of saponins in P. grandiflorus. The study not only elucidates the genetic mechanisms underlying saponin biosynthesis but also provides a theoretical foundation for improving saponin content in P. grandiflorus through genetic engineering and advanced cultivation practices.

Dr. Tao Yang, a leading expert in plant biotechnology and one of the corresponding authors of the study, stated, “Our findings mark a significant advancement in understanding the genetic regulation of saponin biosynthesis in P. grandiflorus. The identification of PgbHLH28 as a key regulator opens up new possibilities for enhancing the medicinal value of this plant through targeted genetic modifications.

“This research provides a valuable framework for developing high-saponin-yielding cultivars, which could have substantial implications for the pharmaceutical industry.”

The implications of this study are far-reaching for both the agricultural and pharmaceutical industries. By leveraging the insights gained from this research, scientists can develop new cultivars of P. grandiflorus with enhanced saponin content, thereby increasing the plant’s medicinal value.

This could lead to more effective natural treatments for various health conditions, including inflammatory diseases and viral infections. Furthermore, understanding the genetic regulation of saponin biosynthesis can aid in the cultivation of other medicinal plants, promoting the use of natural compounds in modern medicine and potentially leading to new therapeutic discoveries.