Abstract: The present disclosure provides a method for controlling a humanoid robot using a hierarchical bipedal action model (BAM), the method comprising obtaining a base controller by training in simulation with reinforcement learning, instantiating an initial BAM including a Gamma model configured to generate intermediate goals, a Beta model configured to translate the intermediate goals into task-space actions, and an Alpha model configured to translate the task-space actions and robot state into motor commands, deploying the initial BAM such that at least the Alpha model executes on-board the humanoid robot, causing the humanoid robot to perform an initial task and logging sensor and control data to form a first dataset, based on the first dataset, training at least one policy of the BAM to generate a refined BAM, and deploying the refined BAM to control the humanoid robot autonomously.

Abstract: The present disclosure provides a control system for a humanoid robot comprising a bipedal action model (BAM) with hierarchical architecture including a beta model executing cognitive tasks at lower frequency, ingesting multimodal sensory inputs including visual data and natural language instructions, and an alpha model executing reactive tasks at higher frequency, communicatively coupled to the beta model. The BAM is trained on retargeted robot training data derived from robot-free training data. At runtime, the BAM outputs continuous control commands as parallel-generated action chunks controlling at least 18 degrees of freedom. The system includes a wearable collection apparatus capturing movement data from a human operator without physical connection to the robot, and a retargeting module translating robot-free training data into robot training data by solving embodiment mismatches between human and robot kinematic structures.

Abstract: 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.

Abstract: The present disclosure provides a method for generating annotation data for robotic training using a hierarchical transformer-based model with multiple layers. The transformer-based model includes Alpha models generating low-level control outputs and Beta models generating high-level control outputs. The method receives multimodal input data comprising visual sensor data and natural language instructions, processes this data through the hierarchical transformer-based model to generate annotations at different abstraction levels, wherein Beta models create semantic annotations describing task objectives and Alpha models generate motor command annotations specifying robotic actions, and stores these annotations with the input data to create annotated training data for robotic control systems.