In 2018, Google DeepMind’s AlphaZero program demonstrated its ability to master chess, shogi, and Go through self-directed learning and a specialized algorithm that assessed optimal moves on a defined grid. Now, researchers at Caltech have adapted this idea for autonomous robots, crafting a planning and decision-making control system that enables these robots to navigate their surroundings effectively.
Soon-Jo Chung, Caltech’s Bren Professor of Control and Dynamical Systems and a senior research scientist at JPL (managed by Caltech for NASA), notes, "Our algorithm strategizes by exploring various significant motions, ultimately selecting the best one through dynamic simulation." Their innovative approach is termed Spectral Expansion Tree Search (SETS), as detailed in the December edition of Science Robotics.
Robots, like humanoid assistants for the elderly, must maneuver freely, adapting to obstacles and unexpected situations. John Lathrop, a graduate student and co-lead author, emphasizes the need for SETS to automate decision-making rather than relying on designers to dictate movements. The algorithm employs control theory and linear algebra to maximize a robot’s physical capabilities.
SETS employs a Monte Carlo Tree Search strategy, where potential moves are represented as nodes branching from a central idea. However, in continuous systems, the number of trajectories can quickly become overwhelming. To address this, SETS strikes a balance between exploring new routes and exploiting known effective ones.
Running in just a tenth of a second, SETS can simulate thousands of trajectories to identify the optimal path. It functions across various robotic applications, evidenced by its success in diverse experiments—from drone navigation to assisting drivers and coordinating tethered spacecraft. This innovative work, supported by esteemed agencies, paves the way for future advancements in robotics.
Vocabulary List:
- Autonomous /ɔːˈtɒnəməs/ (adjective): Having the ability to operate independently.
- Algorithm /ˈælɡərɪðəm/ (noun): A process or set of rules to be followed in calculations or problem-solving operations.
- Navigate /ˈnævɪɡeɪt/ (verb): To plan and direct the course of a vehicle or robot.
- Dynamics /daɪˈnæmɪks/ (noun): The forces or processes that produce change or development in a system.
- Optimal /ˈɒptɪməl/ (adjective): Most conducive to a favorable outcome; best.
- Simulation /ˌsɪmjuˈleɪʃən/ (noun): The imitation of a situation or process.
How much do you know?
What did Google DeepMind’s AlphaZero program demonstrate its ability to master in 2018?
What is the name of the approach adapted by researchers at Caltech for autonomous robots?
Which professor at Caltech is mentioned in the text as a key contributor to the research on autonomous robots?
What key concept does SETS employ to maximize a robot’s physical capabilities?
How does SETS handle the overwhelming number of trajectories in continuous systems?
Which type of simulations can SETS conduct to identify the optimal path in a tenth of a second?
Soon-Jo Chung is a graduate student at Caltech.
The success of SETS has been limited to drone navigation experiments only.
SETS uses a Greedy Algorithm for decision-making.
John Lathrop is a co-lead author involved in the development of SETS.
Monte Carlo Tree Search is a strategy employed by SETS to represent potential moves.
Control theory and linear algebra are not utilized by the SETS algorithm.
According to the text, SETS can simulate thousands of trajectories in just a of a second.
John Lathrop emphasized the need for SETS to automate decision-making rather than relying on designers to dictate .
Soon-Jo Chung holds the position of Bren Professor of Control and Dynamical Systems at .
The innovative approach of SETS for autonomous robots is termed .
John Lathrop is a graduate student and author of the research on autonomous robots.
Control theory and linear algebra are employed by SETS to maximize a robot’s physical .