Abstract: Systems and methods are provided for estimating 3D poses of a subject based on tactile interactions with the ground. Test subject interactions with the ground are recorded using a sensor system along with reference information (e.g., synchronized video information) for use in correlating tactile information with specific 3D poses, e.g., by training a neural network based on the reference information. Then, tactile information received in response to a given subject interacting with the ground can be used to estimate the 3D pose of the given subject directly, i.e., without reference to corresponding reference information. Certain exemplary embodiments use a sensor system in the form of a pressure sensing carpet or mat, although other types of sensor systems using pressure or other sensors can be used in various alternative embodiments.

Abstract: A manufacturing system and method involves learning a self-correcting closed-loop control policy through machine reinforcement learning for a manufacturing process that involves on-the-fly adjustment of process parameters to handle inconsistencies in the manufacturing process and material formulations, and controlling operation of a tool configured to interact with or produce a product including dynamically adjusting at least one parameter of the manufacturing process to thereby dynamically adjust operation of the tool based on qualitative performance information derived from at least one sensor applied as feedback to the closed-loop control policy learned through machine reinforcement learning.

Abstract: Systems and methods for optimizing the formulation of materials are provided. The systems and methods employ a data-driven, iterative approach to derivate optimal material formulations. One portion of the system includes a sample automation system that outputs the material samples to be tested, and a second portion of the system includes an optimization engine that analyzes data extracted from the material samples and generates additional formulations for materials to be printed and tested. This process continues so that optimal material formulations can be determined based on desired mechanical properties of the material to be optimized. The optimization engine can further be capable of predicting results of formulation that have not yet been tested and using those predictions to further drive the next suggested materials to be tested.

Abstract: Systems and methods for optimizing the formulation of materials are provided. The systems and methods employ a data-driven, iterative approach to derivate optimal material formulations. One portion of the system includes a sample automation system that outputs the material samples to be tested, and a second portion of the system includes an optimization engine that analyzes data extracted from the material samples and generates additional formulations for materials to be printed and tested. This process continues so that optimal material formulations can be determined based on desired mechanical properties of the material to be optimized. The optimization engine can further be capable of predicting results of formulation that have not yet been tested and using those predictions to further drive the next suggested materials to be tested.

Abstract: Systems and methods are provided for estimating 3D poses of a subject based on tactile interactions with the ground. Test subject interactions with the ground are recorded using a sensor system along with reference information (e.g., synchronized video information) for use in correlating tactile information with specific 3D poses, e.g., by training a neural network based on the reference information. Then, tactile information received in response to a given subject interacting with the ground can be used to estimate the 3D pose of the given subject directly, i.e., without reference to corresponding reference information. Certain exemplary embodiments use a sensor system in the form of a pressure sensing carpet or mat, although other types of sensor systems using pressure or other sensors can be used in various alternative embodiments.

Abstract: Systems and methods for optimizing the formulation of materials are provided. The systems and methods employ a data-driven, iterative approach to derivate optimal material formulations. One portion of the system includes a sample automation system that outputs the material samples to be tested, and a second portion of the system includes an optimization engine that analyzes data extracted from the material samples and generates additional formulations for materials to be printed and tested. This process continues so that optimal material formulations can be determined based on desired mechanical properties of the material to be optimized. The optimization engine can further be capable of predicting results of formulation that have not yet been tested and using those predictions to further drive the next suggested materials to be tested.