Dynamic task assignment amongst communicating humanoid robots
The present disclosure provides a method for coordinating task execution among multiple humanoid robots, comprising receiving a high-level task command, decomposing it into sub-tasks, determining a cost-optimized assignment using a cost-optimized bipedal action model (CoBAM) based on energy consumption, time to completion, and robot capabilities, and transmitting the assignment to assigned robots. The CoBAM comprises a hierarchical architecture including an L2 beta model operating at 1-20 Hz for high-level planning and an L1 alpha model operating at 100-10,000 Hz for continuous control commands. The cost function considers battery levels, physical distances between robot and sub-task locations, and mechanical wear factors associated with specific joint movements.
1 . A method for coordinating task execution among multiple humanoid robots, comprising:
receiving a high-level task command;
decomposing the high-level task command into a plurality of sub-tasks;
determining a cost-optimized assignment of the plurality of sub-tasks to at least one humanoid robot of the multiple humanoid robots based on a cost function that considers at least one of energy consumption, or time to completion; and
transmitting the cost-optimized assignment to the at least one humanoid robot for execution; and
using a bipedal action model to generate output continuous joint torques based in part on the cost-optimized assignment.
2 . The method of claim 1 , wherein the cost function further considers at least one of battery levels of the available humanoid robots, physical distances between robot locations or sub-task locations, or mechanical wear factors associated with specific joint movements required for each sub-task.
3 . The method of claim 1 , wherein the cost-optimized assignment is determined using a cost-optimized bipedal action model, and said cost-optimized bipedal action model includes: (i) a beta model is configured to operate at a frequency between 1-25 Hz and (ii) an alpha model is configured to operate at a frequency between 100-10,000 Hz.
4 . The method of claim 3 , wherein the beta model is deployed on a remote AI system and the alpha model is deployed locally on each humanoid robot of the multiple humanoid robots.
5 . The method of claim 1 , wherein the cost-optimized assignment is determined using a cost-optimized bipedal action model, and said cost-optimized bipedal action model is trained by:
collecting training data that includes human data;
annotating the training data with cost metrics associated with robot actions, the cost metrics include at least one of energy consumption or time to completion; and
training a hierarchical model architecture that includes a beta model and an alpha model using the annotated training data.
6 . The method of claim 5 , wherein the beta model and the alpha model are co-trained end-to-end using a supervised learning technique.
7 . The method of claim 5 , further comprising the step of: splicing the collected training data into segments; and using a transformer-based model to generate written text that describes at least one object or action associated with the segment.
8 . The method of claim 5 , wherein the cost metrics associated with robot actions are determined by a separate machine learning model.
9 . The method of claim 1 , wherein the continuous joint torques are provided as input to a reinforcement learning (RL)-based controller, and wherein said RL-based controller is configured to control at least one actuator contained in said at least one humanoid robot.