- What are the pros and cons of developing humanoid robots (robots designed to resemble and mimic human actions) versus specialized robots (robots designed for specific tasks, like factory arms or delivery drones)?
- Which type of robot do you believe has the greater potential for future applications in industries like healthcare, manufacturing, or home automation?
- How can humanoid robots leverage their human-like form to work more effectively in environments designed for humans (e.g., homes, offices)? Can their resemblance to humans make them more intuitive for human interaction?
- Are humanoid robots unnecessarily complex and costly for many tasks? Do they introduce engineering challenges that specialized robots don’t face?
- How do specialized robots excel at tasks they are designed for? Consider examples like robotic arms in manufacturing or drones for delivery.
- Are specialized robots too narrow in functionality? How does the need for different robots for different tasks create inefficiencies in some industries?
- Which type of robot can scale better across various industries or tasks? Can humanoid robots evolve to take on multiple roles, or do specialized robots hold an advantage in their tailored efficiency?
In my opinion, specialized robots are generally more efficient than humanoid robots for most tasks. While humanoid robots have the potential to be generalized across various tasks, the training and setup time required for them to perform new tasks is significant. For instance, in warehouse management, a humanoid robot designed to lift and move boxes cannot compete with specialized automation systems. These specialized robots are purpose-built for specific tasks and offer greater precision and efficiency.
Humanoid robots face physical limitations due to their human-like form. For example, lifting heavy objects can be difficult because the design constraint of matching human form limits the torque capacity of their motors. Additionally, perception and planning for humanoid robots aren’t advanced enough to allow them to perform tasks at high speeds, unlike specialized industrial robots, which are designed to operate at maximum efficiency in specific environments. Specialized robots in manufacturing, such as robotic arms, can perform repetitive tasks with increased productivity, lower costs, and minimal setup times compared to humanoid robots.
While humanoid robots might offer the advantage of working in environments designed for humans, their complexity and the need for more advanced control systems can make them costlier and less efficient for many tasks. In contrast, specialized robots excel at tailoring performance to particular tasks, which leads to higher efficiency in areas like manufacturing, healthcare, or delivery systems.
Another major requirement that’s always on the minds of industrial players is the ability to reliably and definitively quantify safety. Most industrially deployed (specialized)robotic systems often work in highly structured environments and are highly deterministic or involve minimal stochasticity. Thus, quantifying uncertainty/safety is straightforward.
However, the sheer complexity of humanoids and their highly non-linear dynamics naturally led to the development and use of sophisticated schools of algorithms to meet their sensing, reasoning, and acting requirements. For eg., we have deep learning being heavily used in sensing, and ML/RL for acting and reasoning capabilities. And for many such systems, UQ is a challenge and has been picking up interest lately in the industry and academia. Once we have the means to reliably perform UQ on algorithms that make humanoid robots possible, I think we will see a boom of algorithms that meet those requirements and thus more humanoid robots that run on them!
Here’s one such recent work, that focuses on Uncertainty estimation in DL.
Humanoid robots face significant challenges in industrial environments due to their complex locomotion and safety concerns, making traditional safety regulations inadequate for their use.
Humanoid robots are not necessarily suited for industrial environments, primarily because they introduce many additional challenges, especially regarding bipedal locomotion. For a humanoid robot to walk like a human, each leg requires at least six motors, totaling twelve for both legs, which is quite costly. In contrast, a wheeled platform may only need two motors while having a much greater load-bearing capacity.
In industrial settings, particularly within industrial robot regulations, there are established safety standards. For example, robots must have a red emergency stop button, which, when pressed, causes all motors to lock immediately. However, humanoid robots struggle with this direct stop because they need to maintain dynamic balance; suddenly locking their joints can lead to falls. Therefore, traditional rules for managing robots are not very applicable to humanoid robots. To implement them in industrial scenarios, many safety regulation issues must first be addressed.
Amidst all the hype with many of these humanoid robots, some are doing real work. Figure has deployed theirs in the BMW Spartanburg plant:
For simple and repetitive tasks that humans are currently doing, this is a very easy drop-in solution if the robots are easy enough to train. With time, factories won’t be designed around human assembly, and these robots will likely not be applicable and will be replaced with specially designed robotics for automation.