One of the primary obstacles impeding the widespread application of robotics lies in their lack of problem-solving capabilities. Enabling robots to independently resolve unforeseen situations could facilitate the adoption of autonomous robots in a broad range of applications such as agriculture, healthcare, exploration, and environmental protection.
As robot designers or programmers, we have a tendency to break-down and solve problems for the robots from our own perspectives. When developing a robot, we often translate our understandings of a problem (e.g., through a ‘problem statement’) into a set of structured ‘decision making’ or ‘control’ algorithms, which are then programmed into the robot. As a result, the ‘autonomy’ of today’s robots is limited. The resulting robot behaviors frequently turn out to be brittle and unnatural. Therefore, a great challenge in robotics research is allowing the robots themselves to play a bigger role in solving problems. When new problems arise, these robots are better equipped to address them.
I would like to propose the following research and development areas associated with problem solving in robotics:
- Define problem solving in the robotics context. Unlike the research on decision making, which has well-defined problems such as POMDP (Partially Observable Markov Decision Processes), problem-solving is currently not a well-recognized research topic in robotics.
- Learning from nature. Most examples of problem solving are from nature, exhibited by people, insects, plants, even individual cells when interacting with the environments. The mechanisms that led to the problem-solving ability of these creatures are not clear.
- Swarm robotics. Another place that we may find examples of problem solving are the collective intelligence of natural swarms (e.g., transportation, foraging, construction) and robotic swarms. The emergence of sometimes unexpected global behaviors through local interaction rules can be interpreted as solutions to environmental challenges, which no single agent in the swarm fully comprehends.
- Case based reasoning, imitation learning, and transfer learning. How could past experiences offer guidance for solving new problems?
- Large Language Models (LLMs). Can a pretrained LLM provide “common sense” to a robot’s problem-solving mechanisms?
- Benchmark problem. How do we evaluate the progress made? Can we identify benchmark problems that are experimentally simple to set up yet challenging to resolve?
- Ethical Robotics and Safety. What are the implications when robots can solve their own problems?
Depending on if the research is inspired by human-like or primitive creature-level problem solving abilities, there could be very different pathways toward problem-solving in robotics.